This document provides an introduction to Bayesian analysis and probabilistic modeling. It begins with an overview of Bayes' theorem and common probability distributions used in Bayesian modeling like the Bernoulli, binomial, beta, Dirichlet, and multinomial distributions. It then discusses how these distributions can be used in Bayesian modeling for problems like estimating probabilities based on observed data. Specifically, it explains how conjugate prior distributions allow the posterior distribution to be of the same family as the prior. The document concludes by discussing how neural networks can quantify classification uncertainty by outputting evidence for different classes modeled with a Dirichlet distribution.
Maximizing the Representation Gap between In-domain & OOD examplesJay Nandy
Paper presented in ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning
Paper link: http://www.gatsby.ucl.ac.uk/~balaji/udl2020/accepted-papers/UDL2020-paper-134.pdf
With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.
In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.
Bayesian statistics uses probability to represent uncertainty about unknown parameters in statistical models. It differs from classical statistics in that parameters are treated as random variables rather than fixed unknown constants. Bayesian probability represents a degree of belief in an event rather than the physical probability of an event. The Bayes' formula provides a way to update beliefs based on new evidence or data using conditional probability. Bayesian networks are graphical models that compactly represent joint probability distributions over many variables and allow for efficient inference.
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machin...Simplilearn
This presentation about Random Forest in R will help you understand what is Random Forest, how does a Random Forest work, applications of Random Forest, important terms to know and you will also see a use case implementation where we predict the quality of wine using a given dataset. Random Forest is an ensemble Machine Learning algorithm. Ensemble methods use multiple learning models to gain better predictive results. It operates building multiple decision trees. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Now let us get started and understand what is Random Forest and how does it work.
Below topics are explained in this Random Forest in R presentation :
1. What is Random Forest?
2. How does a Random Forest work?
3. Applications of Random Forest
4. Use case: Predicting the quality of the wine
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbour recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
This document provides an overview of maximum likelihood estimation. It explains that maximum likelihood estimation finds the parameters of a probability distribution that make the observed data most probable. It gives the example of using maximum likelihood estimation to find the values of μ and σ that result in a normal distribution that best fits a data set. The goal of maximum likelihood is to find the parameter values that give the distribution with the highest probability of observing the actual data. It also discusses the concept of likelihood and compares it to probability, as well as considerations for removing constants and using the log-likelihood.
High Dimensional Data Visualization using t-SNEKai-Wen Zhao
Review of the t-SNE algorithm which helps visualizing the high dimensional data on manifold by projecting them onto 2D or 3D space with metric preserving.
Maximizing the Representation Gap between In-domain & OOD examplesJay Nandy
Paper presented in ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning
Paper link: http://www.gatsby.ucl.ac.uk/~balaji/udl2020/accepted-papers/UDL2020-paper-134.pdf
With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.
In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.
Bayesian statistics uses probability to represent uncertainty about unknown parameters in statistical models. It differs from classical statistics in that parameters are treated as random variables rather than fixed unknown constants. Bayesian probability represents a degree of belief in an event rather than the physical probability of an event. The Bayes' formula provides a way to update beliefs based on new evidence or data using conditional probability. Bayesian networks are graphical models that compactly represent joint probability distributions over many variables and allow for efficient inference.
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machin...Simplilearn
This presentation about Random Forest in R will help you understand what is Random Forest, how does a Random Forest work, applications of Random Forest, important terms to know and you will also see a use case implementation where we predict the quality of wine using a given dataset. Random Forest is an ensemble Machine Learning algorithm. Ensemble methods use multiple learning models to gain better predictive results. It operates building multiple decision trees. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Now let us get started and understand what is Random Forest and how does it work.
Below topics are explained in this Random Forest in R presentation :
1. What is Random Forest?
2. How does a Random Forest work?
3. Applications of Random Forest
4. Use case: Predicting the quality of the wine
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbour recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
This document provides an overview of maximum likelihood estimation. It explains that maximum likelihood estimation finds the parameters of a probability distribution that make the observed data most probable. It gives the example of using maximum likelihood estimation to find the values of μ and σ that result in a normal distribution that best fits a data set. The goal of maximum likelihood is to find the parameter values that give the distribution with the highest probability of observing the actual data. It also discusses the concept of likelihood and compares it to probability, as well as considerations for removing constants and using the log-likelihood.
High Dimensional Data Visualization using t-SNEKai-Wen Zhao
Review of the t-SNE algorithm which helps visualizing the high dimensional data on manifold by projecting them onto 2D or 3D space with metric preserving.
Mrbml004 : Introduction to Information Theory for Machine LearningJaouad Dabounou
La quatrième séance de lecture de livres en machine learning.
Vidéo : https://youtu.be/Ab5RvD7ieFg
Elle concernera une brève introduction à la théorie de l'information: Entropy, K-L divergence, mutual Information,... et son application dans la fonction de perte et notamment la cross-entropy.
Lecture de trois livres, dans le cadre de "Monday reading books on machine learning".
Le premier livre, qui constituera le fil conducteur de toute l'action :
Christopher Bishop; Pattern Recognition and Machine Learning, Springer-Verlag New York Inc, 2006
Seront utilisées des parties de deux livres, surtout du livre :
Ian Goodfellow, Yoshua Bengio, Aaron Courville; Deep Learning, The MIT Press, 2016
et du livre :
Ovidiu Calin; Deep Learning Architectures: A Mathematical Approach, Springer, 2020
The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
Statistical models for networks aim to compare observed networks to random graphs in order to assess statistical significance. Simple random graphs are commonly used as a baseline null model but are unrealistic. More developed null models condition on key network structures like degree distribution or mixing patterns to generate more reasonable random graphs for comparison. Network inference problems evaluate whether an observed network exhibits random or non-random properties relative to an appropriate null model.
UMAP is a technique for dimensionality reduction that was proposed 2 years ago that quickly gained widespread usage for dimensionality reduction.
In this presentation I will try to demistyfy UMAP by comparing it to tSNE. I also sketch its theoretical background in topology and fuzzy sets.
An introduction to Bayesian Statistics using Pythonfreshdatabos
This document provides an introduction to Bayesian statistics and inference through examples. It begins with an overview of Bayes' Theorem and probability concepts. An example problem about cookies in bowls is used to demonstrate applying Bayes' Theorem to update beliefs based on new data. The document introduces the Pmf class for representing probability mass functions and working through examples numerically. Further examples involving dice and trains reinforce how to build likelihood functions and update distributions. The document concludes with a real-world example of analyzing whether a coin is biased based on spin results.
The document discusses K-means clustering, an unsupervised machine learning algorithm that partitions observations into k clusters defined by centroids. It compares clustering to classification, noting clustering does not use training data and maps observations into natural groupings. The K-means algorithm is then explained, with the steps of initializing centroids, assigning observations to the closest centroid, revising centroids as cluster means, and repeating until convergence. Applications of clustering in business contexts like banking, retail, and insurance are also briefly mentioned.
Uncertainty Quantification with Unsupervised Deep learning and Multi Agent Sy...Bang Xiang Yong
Presented at MET4FOF Workshop, JULY 2020
I talk about our recent work of combining Bayesian Deep learning with Explainable Artificial Intelligence (XAI) methods. In particular, we look at Bayesian Autoencoders.
1. The document discusses variational inference and how dropout can be interpreted as a Bayesian approximation method. Dropout is shown to be equivalent to placing a variational distribution over the weights of a neural network.
2. Evaluating the evidence lower bound allows dropout to be framed as variational inference. However, this can underestimate model uncertainty. Alternative divergence measures like chi-square and inclusive KL divergences provide upper bounds that help address this issue.
3. Monte Carlo dropout can estimate epistemic uncertainty by approximating predictive distributions during testing. Overall, the document examines how dropout relates to Bayesian deep learning and variational inference through approximations of evidence bounds.
This document provides an introduction to Bayesian networks, including:
- An outline of topics covered such as Bayesian networks, inference, learning Bayesian networks, and software packages
- A definition of Bayesian networks as directed acyclic graphs combined with conditional probability distributions
- An overview of inference techniques in Bayesian networks including belief propagation, junction tree algorithms, and Monte Carlo methods
Bayesian learning uses prior knowledge and observed training data to determine the probability of hypotheses. Each training example can incrementally increase or decrease the estimated probability of a hypothesis. Prior knowledge is provided by assigning initial probabilities to hypotheses and probability distributions over possible observations for each hypothesis. New instances can be classified by combining the predictions of multiple hypotheses, weighted by their probabilities. Even when computationally intractable, Bayesian methods provide an optimal standard for decision making.
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction to dimensionality reduction and reasons for using it. These include dealing with high-dimensional data issues like the curse of dimensionality. It then covers major dimensionality reduction techniques of feature selection and feature extraction. Feature selection techniques discussed include search strategies, feature ranking, and evaluation measures. Feature extraction maps data to a lower-dimensional space. The document outlines applications of dimensionality reduction like text mining and gene expression analysis. It concludes with trends in the field.
Cross-validation is a technique used to evaluate machine learning models by reserving a portion of a dataset to test the model trained on the remaining data. There are several common cross-validation methods, including the test set method (reserving 30% of data for testing), leave-one-out cross-validation (training on all data points except one, then testing on the left out point), and k-fold cross-validation (randomly splitting data into k groups, with k-1 used for training and the remaining group for testing). The document provides an example comparing linear regression, quadratic regression, and point-to-point connection on a concrete strength dataset using k-fold cross-validation. SPSS output for the
This document describes the 5 steps of principal component analysis (PCA):
1) Subtract the mean from each dimension of the data to center it around zero.
2) Calculate the covariance matrix of the data.
3) Calculate the eigenvalues and eigenvectors of the covariance matrix.
4) Form a feature vector by selecting eigenvectors corresponding to largest eigenvalues. Project the data onto this to reduce dimensions.
5) To reconstruct the data, take the transpose of the feature vector and multiply it with the projected data, then add the mean back.
The document discusses data augmentation techniques for improving machine learning models. It begins with definitions of data augmentation and reasons for using it, such as enlarging datasets and preventing overfitting. Examples of data augmentation for images, text, and audio are provided. The document then demonstrates how to perform data augmentation for natural language processing tasks like text classification. It shows an example of augmenting a movie review dataset and evaluating a text classifier. Pros and cons of data augmentation are discussed, along with key takeaways about using it to boost performance of models with small datasets.
This document discusses independent component analysis (ICA) for blind source separation. ICA is a method to estimate original signals from observed signals consisting of mixed original signals and noise. It introduces the ICA model and approach, including whitening, maximizing non-Gaussianity using kurtosis and negentropy, and fast ICA algorithms. The document provides examples applying ICA to separate images and discusses approaches to improve ICA, including using differential filtering. ICA is an important technique for blind source separation and independent component estimation from observed signals.
MLPfit is a tool for designing and training multi-layer perceptrons (MLPs) for tasks like function approximation and classification. It implements stochastic minimization as well as more powerful methods like conjugate gradients and BFGS. MLPfit is designed to be simple, precise, fast and easy to use for both standalone and integrated applications. Documentation and source code are available online.
The document discusses decision trees, which classify data by recursively splitting it based on attribute values. It describes how decision trees work, including building the tree by selecting the attribute that best splits the data at each node. The ID3 algorithm and information gain are discussed for selecting the splitting attributes. Pruning techniques like subtree replacement and raising are covered for reducing overfitting. Issues like error propagation in decision trees are also summarized.
This document discusses probabilistic logic and inference. It defines conditional probability and the product rule. It then shows examples of computing probabilities of events occurring together or conditionally, such as the probability of a toothache or the probability of a cavity given a toothache. The document also defines Bayes' rule for calculating conditional probabilities and shows an example of how a doctor could use it to determine the probability that a patient has meningitis given that they have a stiff neck.
I am Bella A. I am a Statistical Method In Economics Assignment Expert at economicshomeworkhelper.com/. I hold a Ph.D. in Economics. I have been helping students with their homework for the past 9 years. I solve assignments related to Economics Assignment.
Visit economicshomeworkhelper.com/ or email info@economicshomeworkhelper.com.
You can also call on +1 678 648 4277 for any assistance with Statistical Method In Economics Assignments.
This document provides a concise probability cheatsheet compiled by William Chen and others. It covers key probability concepts like counting rules, sampling tables, definitions of probability, independence, unions and intersections, joint/marginal/conditional probabilities, Bayes' rule, random variables and their distributions, expected value, variance, indicators, moment generating functions, and independence of random variables. The cheatsheet is licensed under CC BY-NC-SA 4.0 and the last updated date is March 20, 2015.
Mrbml004 : Introduction to Information Theory for Machine LearningJaouad Dabounou
La quatrième séance de lecture de livres en machine learning.
Vidéo : https://youtu.be/Ab5RvD7ieFg
Elle concernera une brève introduction à la théorie de l'information: Entropy, K-L divergence, mutual Information,... et son application dans la fonction de perte et notamment la cross-entropy.
Lecture de trois livres, dans le cadre de "Monday reading books on machine learning".
Le premier livre, qui constituera le fil conducteur de toute l'action :
Christopher Bishop; Pattern Recognition and Machine Learning, Springer-Verlag New York Inc, 2006
Seront utilisées des parties de deux livres, surtout du livre :
Ian Goodfellow, Yoshua Bengio, Aaron Courville; Deep Learning, The MIT Press, 2016
et du livre :
Ovidiu Calin; Deep Learning Architectures: A Mathematical Approach, Springer, 2020
The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
Statistical models for networks aim to compare observed networks to random graphs in order to assess statistical significance. Simple random graphs are commonly used as a baseline null model but are unrealistic. More developed null models condition on key network structures like degree distribution or mixing patterns to generate more reasonable random graphs for comparison. Network inference problems evaluate whether an observed network exhibits random or non-random properties relative to an appropriate null model.
UMAP is a technique for dimensionality reduction that was proposed 2 years ago that quickly gained widespread usage for dimensionality reduction.
In this presentation I will try to demistyfy UMAP by comparing it to tSNE. I also sketch its theoretical background in topology and fuzzy sets.
An introduction to Bayesian Statistics using Pythonfreshdatabos
This document provides an introduction to Bayesian statistics and inference through examples. It begins with an overview of Bayes' Theorem and probability concepts. An example problem about cookies in bowls is used to demonstrate applying Bayes' Theorem to update beliefs based on new data. The document introduces the Pmf class for representing probability mass functions and working through examples numerically. Further examples involving dice and trains reinforce how to build likelihood functions and update distributions. The document concludes with a real-world example of analyzing whether a coin is biased based on spin results.
The document discusses K-means clustering, an unsupervised machine learning algorithm that partitions observations into k clusters defined by centroids. It compares clustering to classification, noting clustering does not use training data and maps observations into natural groupings. The K-means algorithm is then explained, with the steps of initializing centroids, assigning observations to the closest centroid, revising centroids as cluster means, and repeating until convergence. Applications of clustering in business contexts like banking, retail, and insurance are also briefly mentioned.
Uncertainty Quantification with Unsupervised Deep learning and Multi Agent Sy...Bang Xiang Yong
Presented at MET4FOF Workshop, JULY 2020
I talk about our recent work of combining Bayesian Deep learning with Explainable Artificial Intelligence (XAI) methods. In particular, we look at Bayesian Autoencoders.
1. The document discusses variational inference and how dropout can be interpreted as a Bayesian approximation method. Dropout is shown to be equivalent to placing a variational distribution over the weights of a neural network.
2. Evaluating the evidence lower bound allows dropout to be framed as variational inference. However, this can underestimate model uncertainty. Alternative divergence measures like chi-square and inclusive KL divergences provide upper bounds that help address this issue.
3. Monte Carlo dropout can estimate epistemic uncertainty by approximating predictive distributions during testing. Overall, the document examines how dropout relates to Bayesian deep learning and variational inference through approximations of evidence bounds.
This document provides an introduction to Bayesian networks, including:
- An outline of topics covered such as Bayesian networks, inference, learning Bayesian networks, and software packages
- A definition of Bayesian networks as directed acyclic graphs combined with conditional probability distributions
- An overview of inference techniques in Bayesian networks including belief propagation, junction tree algorithms, and Monte Carlo methods
Bayesian learning uses prior knowledge and observed training data to determine the probability of hypotheses. Each training example can incrementally increase or decrease the estimated probability of a hypothesis. Prior knowledge is provided by assigning initial probabilities to hypotheses and probability distributions over possible observations for each hypothesis. New instances can be classified by combining the predictions of multiple hypotheses, weighted by their probabilities. Even when computationally intractable, Bayesian methods provide an optimal standard for decision making.
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction to dimensionality reduction and reasons for using it. These include dealing with high-dimensional data issues like the curse of dimensionality. It then covers major dimensionality reduction techniques of feature selection and feature extraction. Feature selection techniques discussed include search strategies, feature ranking, and evaluation measures. Feature extraction maps data to a lower-dimensional space. The document outlines applications of dimensionality reduction like text mining and gene expression analysis. It concludes with trends in the field.
Cross-validation is a technique used to evaluate machine learning models by reserving a portion of a dataset to test the model trained on the remaining data. There are several common cross-validation methods, including the test set method (reserving 30% of data for testing), leave-one-out cross-validation (training on all data points except one, then testing on the left out point), and k-fold cross-validation (randomly splitting data into k groups, with k-1 used for training and the remaining group for testing). The document provides an example comparing linear regression, quadratic regression, and point-to-point connection on a concrete strength dataset using k-fold cross-validation. SPSS output for the
This document describes the 5 steps of principal component analysis (PCA):
1) Subtract the mean from each dimension of the data to center it around zero.
2) Calculate the covariance matrix of the data.
3) Calculate the eigenvalues and eigenvectors of the covariance matrix.
4) Form a feature vector by selecting eigenvectors corresponding to largest eigenvalues. Project the data onto this to reduce dimensions.
5) To reconstruct the data, take the transpose of the feature vector and multiply it with the projected data, then add the mean back.
The document discusses data augmentation techniques for improving machine learning models. It begins with definitions of data augmentation and reasons for using it, such as enlarging datasets and preventing overfitting. Examples of data augmentation for images, text, and audio are provided. The document then demonstrates how to perform data augmentation for natural language processing tasks like text classification. It shows an example of augmenting a movie review dataset and evaluating a text classifier. Pros and cons of data augmentation are discussed, along with key takeaways about using it to boost performance of models with small datasets.
This document discusses independent component analysis (ICA) for blind source separation. ICA is a method to estimate original signals from observed signals consisting of mixed original signals and noise. It introduces the ICA model and approach, including whitening, maximizing non-Gaussianity using kurtosis and negentropy, and fast ICA algorithms. The document provides examples applying ICA to separate images and discusses approaches to improve ICA, including using differential filtering. ICA is an important technique for blind source separation and independent component estimation from observed signals.
MLPfit is a tool for designing and training multi-layer perceptrons (MLPs) for tasks like function approximation and classification. It implements stochastic minimization as well as more powerful methods like conjugate gradients and BFGS. MLPfit is designed to be simple, precise, fast and easy to use for both standalone and integrated applications. Documentation and source code are available online.
The document discusses decision trees, which classify data by recursively splitting it based on attribute values. It describes how decision trees work, including building the tree by selecting the attribute that best splits the data at each node. The ID3 algorithm and information gain are discussed for selecting the splitting attributes. Pruning techniques like subtree replacement and raising are covered for reducing overfitting. Issues like error propagation in decision trees are also summarized.
This document discusses probabilistic logic and inference. It defines conditional probability and the product rule. It then shows examples of computing probabilities of events occurring together or conditionally, such as the probability of a toothache or the probability of a cavity given a toothache. The document also defines Bayes' rule for calculating conditional probabilities and shows an example of how a doctor could use it to determine the probability that a patient has meningitis given that they have a stiff neck.
I am Bella A. I am a Statistical Method In Economics Assignment Expert at economicshomeworkhelper.com/. I hold a Ph.D. in Economics. I have been helping students with their homework for the past 9 years. I solve assignments related to Economics Assignment.
Visit economicshomeworkhelper.com/ or email info@economicshomeworkhelper.com.
You can also call on +1 678 648 4277 for any assistance with Statistical Method In Economics Assignments.
This document provides a concise probability cheatsheet compiled by William Chen and others. It covers key probability concepts like counting rules, sampling tables, definitions of probability, independence, unions and intersections, joint/marginal/conditional probabilities, Bayes' rule, random variables and their distributions, expected value, variance, indicators, moment generating functions, and independence of random variables. The cheatsheet is licensed under CC BY-NC-SA 4.0 and the last updated date is March 20, 2015.
This document provides a probability cheatsheet compiled by William Chen and Joe Blitzstein with contributions from others. It is licensed under CC BY-NC-SA 4.0 and contains information on topics like counting rules, probability definitions, random variables, expectations, independence, and more. The cheatsheet is designed to summarize essential concepts in probability.
The document discusses the multivariate normal distribution. Some key points:
- The multivariate normal distribution generalizes the univariate normal distribution to multiple dimensions. It plays an important role in multivariate analysis.
- The multivariate normal density depends on a mean vector μ and covariance matrix Σ. It takes the form of an exponential function involving the difference between the data vector x and the mean μ, multiplied by the inverse of the covariance matrix Σ.
- Properties of the multivariate normal include: linear combinations of components are normally distributed; subsets are normally distributed; zero covariance implies independence of components; conditional distributions are normal.
This document provides a probability cheatsheet compiled by William Chen and Joe Blitzstein with contributions from others. It is licensed under CC BY-NC-SA 4.0 and contains information on topics like counting rules, probability definitions, random variables, moments, and more. The cheatsheet is regularly updated with comments and suggestions submitted through a GitHub repository.
- The document discusses multivariate statistical analysis techniques including principal component analysis (PCA) and factor analysis.
- PCA involves identifying linear combinations of original variables that maximize variance and are uncorrelated. The first principal component explains the most variance, followed by subsequent components.
- PCA transforms the data to a new coordinate system defined by the eigenvectors of the covariance matrix to extract important information from the data in a lower dimensional representation.
This document discusses concepts related to error analysis and statistics. It covers accuracy and precision, individual measurement uncertainty including means, variance, standard deviation and confidence intervals. It also discusses uncertainty when calculating quantities from multiple measurements using error propagation. Additionally, it discusses least squares fitting of data. Key points include how to quantify accuracy and precision, characterize the distribution of data, calculate uncertainty intervals, and propagate errors through calculations involving multiple measured variables.
I am Driss Fumio. I am a Multivariate Methods Assignment Expert at statisticsassignmentexperts.com. I hold a Master’s Degree in Statistics, from New Brunswick University, Canada. I have been helping students with their assignments for the past 14 years. I solve assignments related to Multivariate Methods. Visit statisticsassignmentexperts.com or email info@statisticsassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Multivariate Methods Assignments.
1. The document covers probability axioms and rules including the additive rule, conditional probability, independence, and Bayes' rule. It also defines discrete and continuous random variables and their probability distributions.
2. Important discrete distributions discussed include the Bernoulli distribution for a binary outcome experiment and the binomial distribution for repeated Bernoulli trials.
3. Techniques for counting permutations, combinations, and sequences of events are presented to handle probability problems involving counting.
This document summarizes research on computing stochastic partial differential equations (SPDEs) using an adaptive multi-element polynomial chaos method (MEPCM) with discrete measures. Key points include:
1) MEPCM uses polynomial chaos expansions and numerical integration to compute SPDEs with parametric uncertainty.
2) Orthogonal polynomials are generated for discrete measures using various methods like Vandermonde, Stieltjes, and Lanczos.
3) Numerical integration is tested on discrete measures using Genz functions in 1D and sparse grids in higher dimensions.
4) The method is demonstrated on the KdV equation with random initial conditions. Future work includes applying these techniques to SPDEs driven
This document contains permissions and copyright information for Chapter 2 of the Handbook of Applied Cryptography. It grants permission to retrieve, print, and store a single copy of this chapter for personal use, but does not extend permission to bind multiple chapters, photocopy, produce additional copies, or make electronic copies available without prior written permission. Except as specifically permitted, the standard copyright from CRC Press applies and prohibits reproducing or transmitting the book or any part in any form without prior written permission.
This document proposes a method for weakly supervised regression on uncertain datasets. It combines graph Laplacian regularization and cluster ensemble methodology. The method solves an auxiliary minimization problem to determine the optimal solution for predicting uncertain parameters. It is tested on artificial data to predict target values using a mixture of normal distributions with labeled, inaccurately labeled, and unlabeled samples. The method is shown to outperform a simplified version by reducing mean Wasserstein distance between predicted and true values.
This document provides an overview of linear models for classification. It discusses discriminant functions including linear discriminant analysis and the perceptron algorithm. It also covers probabilistic generative models that model class-conditional densities and priors to estimate posterior probabilities. Probabilistic discriminative models like logistic regression directly model posterior probabilities using maximum likelihood. Iterative reweighted least squares is used to optimize logistic regression since there is no closed-form solution.
This document provides an overview of some basic mathematics concepts for machine learning, including:
1. Probability theory - definitions of probability, joint and conditional probability, Bayes' rule, expectations.
2. Linear algebra - definitions of vectors, matrices, matrix multiplication and properties, inverses, eigenvalues.
3. Differentiation - definitions of the derivative, gradient, maxima/minima, approximations, the chain rule.
This document presents an overview of independent component analysis (ICA). It begins with notation for factor analysis and ICA models, then discusses assumptions and challenges of ICA including rotation ambiguity and non-Gaussianity. Methods for estimating the ICA model including measuring non-Gaussianity via kurtosis, entropy, and negentropy are described. A direct approach to ICA called product density ICA is outlined along with its optimization algorithm. Finally, an example application to handwritten digit images is briefly discussed.
The document provides information about binomial probability distributions including:
- Binomial experiments have a fixed number (n) of independent trials with two possible outcomes and a constant probability (p) of success.
- The binomial probability distribution gives the probability of getting exactly x successes in n trials. It is calculated using the binomial coefficient and p and q=1-p.
- The mean, variance and standard deviation of a binomial distribution are np, npq, and √npq respectively.
- Examples demonstrate calculating probabilities of outcomes for binomial experiments and determining if results are significantly low or high using the range rule of μ ± 2σ.
1. The document discusses statistical estimation and properties of estimators such as bias, variance, consistency, and asymptotic normality.
2. Key concepts covered include unbiasedness, mean squared error, relative efficiency, sufficiency, and properties of estimators like consistency, asymptotic unbiasedness, and best asymptotic normality.
3. Examples are provided to illustrate theoretical estimators for parameters like the variance of a distribution or coefficients in a linear regression model.
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Argumentation and Machine Learning: When the Whole is Greater than the Sum of...Federico Cerutti
Tutorial at IJCAI 2019
Argumentation technology is a rich interdisciplinary area of research that has emerged as one of the most promising paradigms for common sense reasoning and conflict resolution. In this tutorial I will explore the elements underpinning the vast majority of the approaches in argumentation theory: this brings to light the connections among the various disciplines involved in argumentation theory, from epistemology, to law studies, to complexity theory. I will discuss the most recent real-world research grade prototypes, which present innovative ways for applying well-established theories, and enlarge the scope of applications for argumentation theory, from legal reasoning to sense-making in intelligence analysis. I will then discuss how machine learning approaches are useful for addressing both the knowledge acquisition problem as well as the identification of the most suitable algorithms for argumentative reasoning. The knowledge acquisition problem in argumentation theory is mostly an instance of argument mining tasks, actively studied by researchers both in the argumentation community, and in the natural language processing community. I will discuss the current stage of algorithms for computing semantics extensions—sets of collectively acceptable arguments—of argumentation frameworks, and show the results of recent investigations on the use of machine learning techniques for improving the performance of argumentation algorithms.
Finally, I will discuss the current state-of-the-art approaches using argumentation as part of their architecture. Some of them leverage argumentation technology as a regulariser in learning. Most use argumentation to support explainability and algorithmic accountability. With this tutorial the attendees will acquire a deep and comprehensive understanding of the state-of-the-art of technological capabilities of argumentation technology, and of the synergy already envisaged between it and machine learning. This is particularly important given the current interest from research funding agencies in argument mining and explainable AI.
Human-Argumentation Experiment Pilot 2013: Technical MaterialFederico Cerutti
Technical appendix to the paper: "Formal Arguments, Preferences, and Natural Language Interfaces to Humans: an Empirical Evaluation" by Federico Cerutti, Nava Tintarev, Nir Oren, ECAI 2014, Pages 207 - 212,
DOI10.3233/978-1-61499-419-0-207.
http://ebooks.iospress.nl/volumearticle/36941
Abstract of the paper:
It has been claimed that computational models of argumentation provide support for complex decision making activities in part due to the close alignment between their semantics and human intuition. In this paper we assess this claim by means of an experiment: people's evaluation of formal arguments --- presented in plain English --- is compared to the conclusions obtained from argumentation semantics. Our results show a correspondence between the acceptability of arguments by human subjects and the justification status prescribed by the formal theory in the majority of the cases. However, post-hoc analyses show that there are some significant deviations, which appear to arise from implicit knowledge regarding the domains in which evaluation took place. We argue that in order to create argumentation systems, designers must take implicit domain specific knowledge into account.
Probabilistic Logic Programming with Beta-Distributed Random VariablesFederico Cerutti
by Federico Cerutti; Lance Kaplan; Angelika Kimmig; Murat Sensoy
Paper accepted at AAAI2019
We enable aProbLog—a probabilistic logical programming
approach—to reason in presence of uncertain probabilities
represented as Beta-distributed random variables. We
achieve the same performance of state-of-the-art algorithms
for highly specified and engineered domains, while simultaneously
we maintain the flexibility offered by aProbLog
in handling complex relational domains. Our motivation is
that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision
making under uncertainty: unfortunately, these probability
distributions can be highly uncertain due to sparse data. To
understand and accurately manipulate such probability distributions
we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution
of probabilities representing all the possible values of a
probability when the exact value is unknown.
Supporting Scientific Enquiry with Uncertain SourcesFederico Cerutti
In this paper we propose a computational methodology
for assessing the impact of trust associated to sources of
information in scientific enquiry activities—i.e. relating relevant
information and form logical conclusions, as well as identifying
gaps in information in order to answer a given query. Often trust
in the source of information serves as a proxy for evaluating the
quality of the information itself, especially in the cases of information
overhead. We show how our computational methodology
support human analysts in situational understanding, as well as
highlighting issues that demand further investigation.
This document provides an introduction to formal argumentation theory and summarizes several key concepts:
- It discusses argumentation frameworks consisting of a set of arguments and attacks between arguments. Various semantics are used to identify acceptable sets of arguments.
- Some important semantics properties are outlined, including conflict-freeness, admissibility, strong admissibility, reinstatement, I-maximality, and directionality. Different semantics satisfy different combinations of these properties.
- References are provided for works on argumentation semantics by Dung, Baroni et al., and others that formally define argumentation frameworks and semantics.
Handout: Argumentation in Artificial Intelligence: From Theory to PracticeFederico Cerutti
Handouts for the IJCAI 2017 tutorial on Argumentation. This document is a collection of technical definitions as well as examples of various topics addressed in the tutorial. It is not supposed to be an exhaustive compendium of twenty years of research in argumentation theory.
This material is derived from a variety of publications from many researchers who hold the copyright and any other intellectual property of their work. Original publications are thoroughly cited and reported in the bibliography at the end of the document. Errors and misunderstandings rest with the author of this tutorial: please send an email to federico.cerutti@acm.org for reporting any.
Argumentation in Artificial Intelligence: From Theory to PracticeFederico Cerutti
Argumentation technology is a rich interdisciplinary area of research that, in the last two decades, has emerged as one of the most promising paradigms for commonsense reasoning and conflict resolution in a great variety of domains.
In this tutorial we aim at providing PhD students, early stage researchers, and experts from different fields of AI with a clear understanding of argumentation in AI and with a set of tools they can start using in order to advance the field.
Part 1 of 2
Handout for the course Abstract Argumentation and Interfaces to Argumentative...Federico Cerutti
This document provides an overview of abstract argumentation frameworks and semantics. It begins with definitions of Dung's argumentation framework (AF), including concepts like conflict-free sets, acceptable arguments, and admissible sets. It then covers properties that argumentation semantics can satisfy, like being conflict-free or reinstating acceptable arguments. Several semantics are defined, like complete, grounded, preferred and stable extensions. The document also discusses labelling-based representations of semantics and computational properties of decision problems for different semantics. In the second half, it outlines implementations, ranking-based semantics, argumentation schemes, semantic web argumentation, and natural language interfaces for argumentation systems.
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Handouts for the IJCAI 2015 tutorial on Argumentation.
This document is a collection of technical definitions as well as examples of various topics addressed in the tutorial. It is not supposed to be an exhaustive compendium of twenty years of research in argumentation theory.
This material is derived from a variety of publications from many researchers who hold the copyright and any other intellectual property of their work. Original publications are thoroughly cited and reported in the bibliography at the end of the document. Errors and misunderstandings rest with the author of this tutorial: please send an email to federico.cerutti@acm.org for reporting any.
Argumentation in Artificial Intelligence: 20 years after Dung's work. Right m...Federico Cerutti
Handouts for the IJCAI 2015 tutorial on Argumentation.
This document is a collection of technical definitions as well as examples of various topics addressed in the tutorial. It is not supposed to be an exhaustive compendium of twenty years of research in argumentation theory.
This material is derived from a variety of publications from many researchers who hold the copyright and any other intellectual property of their work. Original publications are thoroughly cited and reported in the bibliography at the end of the document. Errors and misunderstandings rest with the author of this tutorial: please send an email to federico.cerutti@acm.org for reporting any.
A tutorial by Federico Cerutti
http://scienceartificial.com
Slides of the tutorial given at IJCAI 2015 http://ijcai-15.org/
Website for the tutorial: http://scienceartificial.com/IJCAI2015tutorial
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Computational trust mechanisms aim to produce a trust rating from both direct and indirect information about agents behaviour. J\o sang’s Subjective Logic has been widely adopted as the core of such systems via its fusion and discount operators. Recently we proposed an operator for discounting opinions based on geometrical properties, and, continuing this line of investigation, this paper describes a new geometry based fusion operator. We evaluate this fusion operator together with our geometric discount operator in the context of a trust system, and show that our operators outperform those originally described by J\o sang. A core advantage of our work is that these operators can be used without modifying the remainder of the trust and reputation system
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This paper presents a novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances. The approach is based on the idea of reducing the problem of computing complete extensions to a SAT problem and then using a depth-first search method to derive preferred extensions. The proposed approach has been tested using two distinct SAT solvers and compared with three state-of-the-art systems for preferred extension computation. It turns out that the proposed approach delivers significantly better performances in the large majority of the considered cases.
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5. Suppose we randomly pick one of the boxes and from that
box we randomly select an item of fruit, and having observed
which sort of fruit it is we replace it in the box from which it
came.
We could imagine repeating this process many times. Let us
suppose that in so doing we pick the red box 40% of the time
and we pick the blue box 60% of the time, and that when we
remove an item of fruit from a box we are equally likely to
select any of the pieces of fruit in the box.
We are told that a piece of fruit has been selected and it is an orange.
Which box does it came from?
5
Image from Bishop, C. M. Pattern Recognition and Machine Learning. (Springer-Verlag, 2006).
7. Given the parameters of our model w, we can capture our assumptions about w, before
observing the data, in the form of a prior probability distribution p(w). The effect of the
observed data D = {t1, . . . , tN } is expressed through the conditional p(D |w), hence Bayes
theorem takes the form:
p(w|D ) =
likelihood
p(D |w)
prior
p(w)
p(D )
(4)
posterior ∝ likelihood · prior (5)
p(D ) = p(D |w)p(w)dw (6)
It ensures that the posterior distribution on the left-hand side is a valid probability density and integrates to one.
7
8. Frequentist paradigm
• w is considered to be a fixed parameter,
whose values is determined by some
form of estimator, e.g. the maximum
likelihood in which w is set to the value
that maximises p(D |w)
• Error bars on this estimate are obtained
by considering the distribution of
possible data sets D .
• The negative log of the likelihood
function is called an error function: the
negative log is a monotonically
decreasing function hence maximising
the likelihood is equivalent to
minimising the error.
Bayesian paradigm
• There is only one single data set D (the
one observed) and the uncertainty in the
parameters is expressed through a
probability distribution over w.
• The inclusion of prior knowledge arises
naturally: suppose that a fair-looking
coin is tossed three times and lands
heads each time. A classical maximum
likelihood estimate of the probability of
landing heads would give 1.
There are cases where you want to
reduce the dependence on the prior,
hence using noninformative priors.
8
9. Binary variable: Bernoulli
Let us consider a single binary random variable x ∈ {0, 1}, e.g. flipping coin, not necessary
fair, hence the probability is conditioned by a parameter 0 ≤ µ ≤ 1:
p(x = 1|µ) = µ (7)
The probability distribution over x is known as the Bernoulli distribution:
Bern(x|µ) = µx
(1 − µ)1−x
(8)
E[x] = µ (9)
9
10. Binomial distribution
The distribution of m observations of x = 1 given the datasize N is given by the Binomial
distribution:
Bin(m|N, µ) =
N
m
µm
(1 − µ)N−m
(10)
with
E[m] ≡
N
m=0
mBin(m|N, µ) = Nµ (11)
and
var[m] ≡
N
m=0
(m − E[m])2
Bin(m|N, µ) = Nµ(1 − µ) (12)
10
Image from Bishop, C. M. Pattern Recognition and Machine Learning. (Springer-Verlag, 2006).
11. How many times, over N = 10 runs, would you see x = 1 if µ = 0.25?
m
0 1 2 3 4 5 6 7 8 9 10
0
0.1
0.2
0.3
11
12. Let’s go back to the Bernoulli distribution
Now suppose that we have a data set of observations x = (x1, . . . , xN )T
drawn independently
from a Bernoulli distribution (iid) whose mean µ is unknown, and we would like to
determine this parameter from the data set.
p(D |µ) =
N
n=1
p(xn|µ) =
N
n=1
µxn
(1 − µ)1−xn
(13)
Let’s maximise the (log)-likelihood to identify the parameter (log simplifies and reduces risks
of underflow):
ln p(D |µ) =
N
n=1
ln p(xn|µ) =
N
n=1
{xn ln µ + (1 − xn) ln(1 − µ)} (14)
12
13. The log likelihood depends on the N observations xn only through their sum
n
xn, hence
the sum provides an example of a sufficient statistics for the data under this distribution,
“
hence no other statistic that can be calculated from the same sample provides
any additional information as to the value of the parameter
Fisher 1922„
13
14. d
dµ
ln p(D |µ) = 0
N
n=1
xn
µ
−
1 − xn
1 − µ
= 0
N
n=1
xn − µ
µ(1 − µ)
= 0
N
n=1
xn = Nµ
µML =
1
N
N
n=1
xn
aka sample mean. Risk of overfit: consider to toss the coin three times and each time is head
14
15. In order to develop a Bayesian treatment to the overfit problem of the maximum likelihood
estimator for the Bernoulli. Since the likelihood takes the form of the product of factors of
the form µx
(1 − µ)1−x
, if we choose a prior to be proportional to powers of µ and (1 − µ) then
the posterior distribution, proportional to the product of the prior and the likelihood, will
have the same functional form as the prior. This property is called conjugacy.
15
16. Binary variables: Beta distribution
Beta(µ|a, b) =
Γ(a + b)
Γ(a)Γ(b)
µa−1
(1 − µ)b−1
with
Γ(x) ≡
∞
0
ux−1
e−u
du
E[µ] =
a
a + b
var[µ] =
ab
(a + b)2(a + b + 1)
a and b are hyperparameters controlling the distribution of parameter µ.
16
17. µ
a = 0.1
b = 0.1
0 0.5 1
0
1
2
3
µ
a = 1
b = 1
0 0.5 1
0
1
2
3
µ
a = 2
b = 3
0 0.5 1
0
1
2
3
µ
a = 8
b = 4
0 0.5 1
0
1
2
3
17
Images from Bishop, C. M. Pattern Recognition and Machine Learning. (Springer-Verlag, 2006).
18. Considering a beta distribution prior and the binomial likelihood function, and given
l = N − m
p(µ|m, l, a, b) ∝ µm+a−1
(1 − µ)l+b−1
Hence p(µ|m, l, a, b) is another beta distribution and we can rearrange the normalisation
coefficient as follows:
p(µ|m, l, a, b) =
Γ(m + a + l + b)
Γ(m + a)Γ(l + b)
µm+a−1
(1 − µ)l+b−1
µ
prior
0 0.5 1
0
1
2
µ
likelihood function
0 0.5 1
0
1
2
µ
posterior
0 0.5 1
0
1
2
18
Images from Bishop, C. M. Pattern Recognition and Machine Learning. (Springer-Verlag, 2006).
19. Epistemic vs Aleatoric uncertainty
Aleatoric uncertainty
Variability in the outcome of an experiment
which is due to inherently random effects
(e.g. flipping a fair coin): no additional
source of information but Laplace’s daemon
can reduce such a variability.
Epistemic uncertainty
Epistemic state of the agent using the model,
hence its lack of knowledge that—in
principle—can be reduced on the basis of
additional data samples.
It is a general property of Bayesian learning
that, as we observe more and more data, the
epistemic uncertainty represented by the
posterior distribution will steadily decrease
(the variance decreases).
19
21. Multinomial variables: categorical distribution
Let us suppose to roll a dice with K = 6 faces. An observation of this variable x equivalent
to x3 = 1 (e.g. the number 3 with face up) can be:
x = (0, 0, 1, 0, 0, 0)T
Note that such vectors must satisfy
K
k=1
xk = 1.
p(x|µ) =
K
k=1
µxk
k
where µ = (µ1, . . . , µK )T
, nad the parameters µk are such that µk ≥ 0 and
k
µk = 1.
Generalisation of the Bernoulli
21
22. p(D |µ) =
N
n=1
K
k=1
µxnk
k
The likelihood depends on the N datapoints only through the K quantities
mk =
n
xnk
which represent the number of observations of xk = 1 (e.g. with k = 3, the third face of the
dice). These are called the sufficient statistics for this distribution.
22
23. Finding the maximum likelihood requires a Lagrange multiplier that
K
x=1
mk ln µk + λ
K
k=1
µk − 1
Hence
µML
k =
mk
N
which is the fraction of N observations for which xk = 1.
23
24. Multinomial variables: the Dirichlet distribution
The Dirichlet distribution is the generalisation of the beta distribution to K dimensions.
Dir(µ|α) =
Γ(α0)
Γ(α1) · · · Γ(αK )
K
k=1
µαk −1
k
such that
k
µk = 1, α = (α1, . . . , αK )T
, αk ≥ 0 and
α0 =
K
k=1
αk
24
25. Considering a Dirichlet distribution prior and the categorical likelihood function, the
posterior is then:
p(µ|D , α) = Dir(µ|α + m) =
=
Γ(α0 + N)
Γ(α1 + m1) · · · Γ(αK + mK )
K
k=1
µαk +mk −1
k
The uniform prior is given by Dir(µ|1) and the Jeffreys’ non-informative prior is given by
Dir(µ|(0.5, . . . , 0.5)T
).
The marginals of a Dirichlet distribution are beta distributions.
25
27. Change the loss function so to output pieces of evidences in favour of different classes that
should then be considered through Bayesian update resulting into a Dirichlet Distribution
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. “Evidential deep learning to quantify classification
uncertainty.” Advances in Neural Information Processing Systems. 2018.
27
28. From Evidence to Dirichlet
Let us now assume a Dirichlet distribution over K classes that is the result of Bayesian
update with N observations and starting with a uniform prior:
Dir(µ | α) = Dir(µ | e1 + 1, e2 + 2, . . . , eK + 1 )
where ei is the number of observations (evidence) for the class k, and
k
ek = N.
28
29. Dirichlet and Epistemic Uncertainty
The epistemic uncertainty associated to a Dirichlet distribution Dir(µ | α) is given by
u =
K
S
with K the number of classes and S = α0 =
K
k=1
αk is the Dirichlet strength.
Note that if the Dirichlet has been computed as the resulting of Bayesian update from a
uniform prior, 0 ≤ u ≤ 1, and u = 1 implies that we are considering the uniform distribution
(an extreme case of Dirichlet distribution).
Let us denote with µk
αk
S
.
29
30. Loss function
If we then consider Dir(mi | αi ) as the prior for a Multinomial p(yi | µi ), we can then compute the
expected squared error (aka Brier score)
E[ yi − mi
2
2
] =
K
k=1
E[y2
i,k − 2yi,k µi,k + µ2
i,k ] =
X
k=1
y2
i,k − 2yi,k E[µi,k ] + E[µ2
i,k ] =
=
K
k=1
y2
i,k − 2yi,k E[µi,k ] + E[µi,k ]2
+ var[µi,k ] =
=
K
k=1
(yi,k − E[µi,k ])2
+ var[µi,k ] =
=
K
k=1
yi,k −
αi,k
Si
2
+
αi,k (Si − αi,k )
S2
i (Si + 1)
=
=
K
k=1
(yi,k − µi,k )2
+
µi,k (1 − µi,k )2
Si + 1
The loss over a batch of training samples is the sum of the loss for each sample in the batch.
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. “Evidential deep learning to quantify classification
uncertainty.” Advances in Neural Information Processing Systems. 2018.
30
31. Learning to say “I don’t know”
To avoid generating evidence for all the classes when the network cannot classify a given
sample (epistemic uncertainty), we introduce a term in the loss function that penalises the
divergence from the uniform distribution:
L =
N
i=1
E[ yi − µi
2
2
] + λt
N
i=1
KL ( Dir(µi | αi ) || Dir(µi | 1) )
where:
• λt is another hyperparameter, and the suggestion is to use it parametric on the number of
training epochs, e.g. λt = min 1,
t
CONST
with t the number of current training epoch, so that
the effect of the KL divergence is gradually increased to avoid premature convergence to the
uniform distribution in the early epoch where the learning algorithm still needs to explore the
parameter space;
• αi = yi + (1 − yi ) · αi are the Dirichlet parameters the neural network in a forward pass has put
on the wrong classes, and the idea is to minimise them as much as possible.
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. “Evidential deep learning to quantify classification
uncertainty.” Advances in Neural Information Processing Systems. 2018.
31
32. KL recap
Consider some unknown distribution p(x) and suppose that we have modelled this using
q(x). If we use q(x) instead of p(x) to represent the true values of x, the average additional
amount of information required is:
KL(p||q) = − p(x) ln q(x)dx − − p(x) ln p(x)dx
= − p(x) ln
q(x)
p(x)
dx
= −E ln
q(x)
p(x)
(15)
This is known as the relative entropy or Kullback-Leibler divergence, or KL divergence
between the distributions p(x) and q(x).
Properties:
• KL(p||q) ≡ KL(q||p);
• KL(p||q) ≥ 0 and KL(p||q) = 0 if and only if p = q
32
33. KL ( Dir(µi | αi ) || Dir(µi | 1) ) = ln
Γ( K
k=1 αi,k )
Γ(K) K
k=1 Γ(αi,k )
+
K
k=1
(αi,k −1)
ψ(αi,k ) − ψ
K
j=1
αi,j
where ψ(x) =
d
dx
ln ( Γ(x) ) is the digamma function
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. “Evidential deep learning to quantify classification
uncertainty.” Advances in Neural Information Processing Systems. 2018.
33
34. EDL and robustness to FGS
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. “Evidential deep learning to quantify classification
uncertainty.” Advances in Neural Information Processing Systems. 2018.
34
35. EDL + GAN for adversarial training
Sensoy, Murat, et al. “Uncertainty-Aware Deep Classifiers using Generative Models.” AAAI 2020
35
36. aluation
VAE + GAN
G
D' D
For each data point in latent space, we generate a new noisy sample, which is similar
to it to some extent. Hence, we avoid mode-collapse problem.
be trivially predicted without learning the actual structure of the data. Similarly,
if the noise distribution is too close to the data distribution, the density ratio
would be trivially one and the learning will be deprived.
G: Generator in the latent space of VAE
D’: Discriminator in the latent space
D : Discriminator in the input space
Figure 2: Original training samples (top), samples recon-
structed by the VAE (middle), and the samples generated by
the proposed method (bottom) over a number of epochs.
for high dimensional data by maximizingSensoy, Murat, et al. “Uncertainty-Aware Deep Classifiers using Generative Models.” AAAI 2020
36