Bayesian model averaging
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The document discusses Bayesian model averaging (BMA) as a Bayesian approach to combining multiple models, explaining how to implement BMA using R packages, highlighting that BMA works well for linear models but its application to more complex models is still limited, and concludes by noting that BMA provides useful tools for model interpretation and combination beyond just prediction.
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Machine Learning and computing power have made huge improvements in the last decade. It’s now possible to unlock complex problems in multidimensional space with ensemble, brute force algorithms or deep neural networks, with performances that were unthinkable a few years ago. However the use of black box models is still frown upon in a business setting. In fact the decision functions of those models are often impossible to interpret for humans, can be biased or just based on absurd assumption. What if your risk model denies loans to people on ethnic ground? SHAP comes as an innovative framework to obtain local explanations for the output of a model, making the black box much more transparent.
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Lets take a look at the different technique selection crossover and mutation on an example of a binary problem where the variables in the optimization problem can either take on a value of zero or one.
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The last thing is what’s known as mutation where we take a parent and mutate certain variables to take on random values and we create a child based off of the mutation.
A mutation allow genetic algorithms to avoid falling into local minima and it really helps them explore the solution space well.
So lets see an example of how this might work on an optimization problem of two variables x and y and the three sets of contours that we have here are each different minima to the optimization problem.
Now the green minima and the red minima our local minima while the blue one actually happens to be a global minima.
The yellow dots on here are the initial points , are the first generation for my genetic algorithm.
So the first step that the genetic algorithm does is . it evaluates all these points and determines the fitness function value for each one of them.
The next thing that will do is it will select a few good solution as the parent two continue on to the next generation.
So these green points here did we will keep those for the next generation and will also use them to create the subsequent generation.
So for iteration 2 regenerate those new points through selection crossover and mutation and then we evaluate the new population
We then repeat this process of generating new generation until the algorithm converges.
Genetic algorithms can converges through a variety of convergence criteria.
A couple popular ones are a fixed number of generation so the genetic algorithm will just run until it hit a certain number of generations
Another one is it will converge when the best objective function or best fitness function value is no longer changing or its changing by a really small amount.
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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.
Machine Learning Unit 2 Semester 3 MSc IT Part 2 Mumbai University
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Domain adaptation
This document discusses domain adaptation techniques for machine learning models. It summarizes several papers on domain adaptation methods, including domain-adversarial training which uses a gradient reversal layer to learn domain-invariant features, adversarial discriminative domain adaptation which matches features from source and target domains, and maximum classifier discrepancy which trains generators and classifiers adversarially to preserve decision boundaries during domain adaptation. The document provides an overview of common domain adaptation scenarios and different approaches for supervised, unsupervised, and domain generalization settings.
An Introduction to Soft Computing
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
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Machine Learning Unit 2 Semester 3 MSc IT Part 2 Mumbai University
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Customer segmentation - Games Analytics and Business Intelligence, Sep 2015
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http://theinnovationenterprise.com/summits/digital-web-analytics-summit-london-2015/schedule
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We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself.
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3. Clean code in Jupyter notebooks reads like well-written prose, with each cell presenting one clear idea, execution, and output.
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4Front Game Data Science
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KPIs for Mobile Game Soft Launch
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Video: https://youtu.be/yR-Vg23s7F4
http://zennaapps.com/
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This document discusses setting up an environment for agile data science and analytics applications. It recommends:
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This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
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原版一模一样【微信：741003700 】【美国佩斯大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
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Male reproduction physiology by Suyash Garg .pptx
Physiology of Male reproduction.
Video mentioned at page no. 23 as summary for better understanding
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Anti-Universe And Emergent Gravity and the Dark Universe
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
Reaching the age of Adolescence- Class 8
This is a presentation on understanding the age of adolescence.
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptx
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
fermented food science of sauerkraut.pptx
This ppt contains the production of a fermented food name - sauerkraut
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Compositions of iron-meteorite parent bodies constrainthe structure of the pr...
Magmatic iron-meteorite parent bodies are the earliest planetesimals in the Solar System,and they preserve information about conditions and planet-forming processes in thesolar nebula. In this study, we include comprehensive elemental compositions andfractional-crystallization modeling for iron meteorites from the cores of five differenti-ated asteroids from the inner Solar System. Together with previous results of metalliccores from the outer Solar System, we conclude that asteroidal cores from the outerSolar System have smaller sizes, elevated siderophile-element abundances, and simplercrystallization processes than those from the inner Solar System. These differences arerelated to the formation locations of the parent asteroids because the solar protoplane-tary disk varied in redox conditions, elemental distributions, and dynamics at differentheliocentric distances. Using highly siderophile-element data from iron meteorites, wereconstruct the distribution of calcium-aluminum-rich inclusions (CAIs) across theprotoplanetary disk within the first million years of Solar-System history. CAIs, the firstsolids to condense in the Solar System, formed close to the Sun. They were, however,concentrated within the outer disk and depleted within the inner disk. Future modelsof the structure and evolution of the protoplanetary disk should account for this dis-tribution pattern of CAIs.
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Bayesian model averaging
- 1. Volodymyrk Bayesian Model Averaging Bayesian Mixer, 27.09.2016 London, UK
- 2. Volodymyrk Bayesian Model Averaging (BMA) - 1 minute version New Project - how much does it worth? CFO VP of Growth Net Present Value: $50m $100m Model M1 Model M2 30%CEO belief: after evaluating both models and market data 70% $15m + $70m = $85m K = 2
- 3. Volodymyrk Bayesian Model Averaging (BMA) - 3 minute version VP of Growth CLV assumptions $10 $12 $15 CAC $4 72 129 149 $6 62 112 133 $8 51 92 101 Average= $100.11m Sensitivity Analysis for M2 DATA
- 4. Volodymyrk Bayesian Model Averaging (BMA) - 5 minute version Bayesian Model Averaging: A Tutorial Jennifer A. Hoeting, David Madigan, Adrian E. Raftery and Chris T. Volinsky How much do you trust your VP and CFO, before you look at models? Scary normalising term that you can ignore Prior probability for model parameter
- 5. Volodymyrk Bayesian answer to overfitting Frequentist: - model selection - regularisation Bayesian: - BMA - marginalisation
- 6. Volodymyrk Case Study You just get the best job in the galaxy
- 7. Volodymyrk Your new Boss Business domain Modelling case Always test your models on synthetic data that you understand and control
- 8. Volodymyrk Use Cases: - Fraud Detection - Inventory Sourcing Data
- 9. Volodymyrk Modelling goals - Prediction range is needed, so that you can identify fraudulent transactions (sand people under-reporting real transaction size and pocketing profit) - Sale price should be easily explainable, as a function of various Droid Features so that Jabba can invest in appropriate scavenging/sourcing projects - You want lowest prediction error possible so that you are not feeded to Sarlacc
- 12. Volodymyrk Model Selection - classical method credits ~ height + weight + power + dents + rad + wheels + legs + red + blue + black + temperature + lat + long + ir_emit + dents_log + height_log + weight_log + power_log + rad_log Adj. R2: 0.884974385182
- 13. Volodymyrk Model Selection - backward elimination
- 14. Volodymyrk Final Model credits ~ weight + power + dents + rad + wheels + blue + black + temperature + lat + dents_log + height_log + weight_log + power_log Adj. R2: 0.903544333611
- 16. Volodymyrk Ridge regression (L2 regularisation)
- 17. Volodymyrk Bayesian Model Averaging for Linear Models - a special case Inclusion probability for (regression coefficients) are weighted across all possible models Number of models = combinations of all K features (include/exclude) = 2K
- 18. Volodymyrk How to actually do BMA? (in R) cran.r-project.org/web/packages/BMA cran.r-project.org/web/packages/BAScran.r-project.org/web/packages/BMS Mature. A.k.a. “the original” Developed by PhD duringresearch. Not maintained Newest. Maintained by Chair of the Department ofStatistical Science at Duke
- 19. Volodymyrk BMA using BMS (R) package Model Selection L2 Regularisation BMA MSE 9736.49 7782.21 7329.44 It worked! But you can find inputs into data generator script that will not work as well!
- 20. Volodymyrk Nice things you get from BMA Posterior Inclusion Probability! How cool is that!
- 21. Volodymyrk Model ranking! MCMC can beused, if number of features is large Best model, according toBMA
- 22. Volodymyrk Can we use it for more complex models? normalising term that you can ignore http://www.ssc.wisc.edu/~bhansen/718/NonParametrics15.pdf http://www.ejwagenmakers.com/2004/aic.pdf Warning:Very questionable math. Does not work
- 23. Volodymyrk Can we use BMA to combine complex (incl. hierarchical) models? 1 3 2 Model order is somewhat similar. Relative probabilities are not. We need working Reverse-Jump MCMC or something more sophisticated. Not available in common bayesian MCMC packages yet.
- 24. Volodymyrk In Summary - BMA is a Bayesian version of ML Model Ensembles - Math behind is quite beautiful - Model Averaging is useful for interpretation, not only prediction - Invest in synthetic data generation, - before applying new modelling techniques to real-world data - Even if you are not using BMA, fit different models - And combine them, if your goal is prediction - BMA works very well for common GLMs, but does not work yet for arbitrary models - Do try it next time you need to fit OLS, though!
- 25. Volodymyrk Of course we are hiring! ● (Snr, Mid) Data Scientists ● Solutions Architect ● Ruby Developer ● Data Engineer ● Senior Artist ● Technical Artist ● Unity Developers ● Senior Product Manager ● Product Director http://jobs.productmadness.com/