•

4 likes•11,168 views

Basic concepts of statistical inference. Outline: stochastic variables, frequency functions, expectations, variance, entropy, joint probabilities, conditional probabilities, independence, sampling, estimation, maximum likelihood estimation (MLE), smoothing, hypothesis testing,z-test,

Report

Share

Report

Share

Download to read offline

Hypothesis Testing

Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )

Confidence Intervals: Basic concepts and overview

This document provides an overview of confidence intervals. It defines confidence intervals and describes their use in statistical inference to estimate population parameters. It explains that a confidence interval provides a range of plausible values for an unknown population parameter based on a sample statistic. The document outlines the key steps in calculating a confidence interval, including determining the point estimate, standard error, and critical value corresponding to the desired confidence level. It discusses how the width of the confidence interval indicates the precision of the estimate and is affected by factors like the sample size and population variability.

Statistical inference

This document discusses statistical inference, which involves drawing conclusions about an unknown population based on a sample. There are two main types of statistical inference: parameter estimation and hypothesis testing. Parameter estimation involves obtaining numerical values of population parameters from a sample, like estimating the percentage of people aware of a product. Hypothesis testing involves making judgments about assumptions regarding population parameters based on sample data. The document also discusses point estimation, interval estimation, standard error, and provides examples of calculating confidence intervals.

Probability ppt by Shivansh J.

The document discusses probability and chance. It defines probability as a measure of how likely an event is to occur from 0 to 1, with 1 being certain and 0 being impossible. Chance is expressed as a percentage, with 50% meaning equally likely. Examples are given of probability in weather forecasting and games. The origins and modern uses of probability are outlined in fields like traffic control, genetics, and investment returns. Predictable versus unpredictable events are distinguished. Formulae for calculating probability from sample data are provided. Random phenomena are described as having uncertain individual outcomes but regular relative frequencies over many repetitions, like coin tosses. Applications in risk assessment and commodity markets are mentioned. Reliability engineering in product design is discussed as using probability of

Chi square tests using SPSS

Chi-Square test for independence of attributes / Chi-Square test for checking association between two categorical variables, Chi-Square test for goodness of fit

Poisson distribution

The document discusses the Poisson distribution, which models rare events. It describes how the Poisson distribution can be used when the number of events is large but the probability of each individual event is small. The key conditions for applying the Poisson distribution are that events occur independently and the rate of occurrence is constant. The mean and variance of the Poisson distribution are equal to the parameter μ, which represents the average number of events. Examples of phenomena that follow a Poisson distribution include traffic accidents, website visits, and product demand. A formula for calculating Poisson probabilities is provided.

Statistical inference concept, procedure of hypothesis testing

This document discusses hypothesis testing in statistical inference. It defines statistical inference as using probability concepts to deal with uncertainty in decision making. Hypothesis testing involves setting up a null hypothesis and alternative hypothesis about a population parameter, collecting sample data, and using statistical tests to determine whether to reject or fail to reject the null hypothesis. The key steps are setting hypotheses, choosing a significance level, selecting a test criterion like t, F or chi-squared distributions, performing calculations on sample data, and making a decision to reject or fail to reject the null hypothesis based on the significance level.

Two Proportions

1. The document discusses hypothesis testing of claims about population parameters such as proportions, means, standard deviations, and variances from one or two samples.
2. Key concepts include hypothesis tests using z-tests, t-tests, and chi-square tests. Confidence intervals are also constructed for parameters.
3. Two examples are provided to demonstrate hypothesis testing of claims about two population proportions using z-tests. The null hypothesis is rejected in one example but not the other.

Estimating a Population Mean

1) The sample shows the mean weight of men is 172.55 lbs with a standard deviation of 26 lbs.
2) A 95% confidence interval for the population mean weight is estimated to be between 164.49 lbs and 180.61 lbs.
3) This suggests that the outdated estimate of 166.3 lbs used for safety capacities is likely an underestimate, and updating to the point estimate of 172.55 lbs could help prevent overloading issues.

Sample size calculation

This document provides information and examples on calculating sample size for clinical studies. It discusses key factors that affect sample size calculation, including minimum important difference, standard deviation, power, type I and II errors, study design, dropout rate, and compliance. It provides step-by-step worked examples of calculating sample size for various hypothetical clinical studies. The document emphasizes that sample size calculation is important to ensure studies are adequately powered and conclusions are valid.

Statistical tests

This document provides an overview of statistical tests and hypothesis testing. It discusses the four steps of hypothesis testing, including stating hypotheses, setting decision criteria, computing test statistics, and making a decision. It also describes different types of statistical analyses, common descriptive statistics, and forms of statistical relationships. Finally, it provides examples of various parametric and nonparametric statistical tests, including t-tests, ANOVA, chi-square tests, correlation, regression, and decision trees.

SAMPLING THEORY AND TECHNIQUES.pdf

This document outlines lecture material on sampling techniques from Dr. Tushar Bhatt of Saurashtra University. It discusses various probability and non-probability sampling methods including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and PPS sampling. For each method, it provides definitions, formulas, steps for implementation, and examples. The document is intended as teaching material, covering core concepts in sampling and how to select samples from different populations.

Z test, f-test,etc

The document discusses the z-test, which is a hypothesis testing procedure that uses the z-statistic. It assumes the events under investigation follow the standard normal distribution. The z-test involves defining the null and alternative hypotheses, choosing the test statistic (the z-statistic), computing the critical region based on the significance level (typically 0.05 or 0.025), and determining whether the test statistic falls in the critical region to reject or fail to reject the null hypothesis. An example problem is provided to demonstrate how to perform a z-test.

Test of-significance : Z test , Chi square test

1) Tests of significance help determine if observed differences between samples are real or due to chance. The null hypothesis assumes no real difference, and significance tests either reject or fail to reject the null hypothesis.
2) Common tests include the Z-test for comparing two proportions, and the chi-square test which can be used for both large and small samples to compare observed and expected frequencies across groups.
3) To perform a significance test, the null hypothesis is stated, a test statistic is calculated (like Z or chi-square), and the p-value determines whether to reject or fail to reject the null hypothesis at a given significance level like 5%.

Lecture 5: Interval Estimation

inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,

Two Means Independent Samples

This document discusses hypothesis testing and constructing confidence intervals for comparing two means from independent populations. It provides:
1. Requirements for using a z-test or t-test to compare two means, including that the samples must be independent and randomly selected, and meet certain size or normality criteria.
2. Formulas and steps for conducting a z-test when population variances are known, and a t-test when they are unknown, to test claims about differences in population means.
3. Instructions for using a calculator to perform two-sample z-tests, t-tests, and to construct confidence intervals for the difference between two means.
4. An example comparing hotel room rates using

Hypothesis Testing

The document discusses hypothesis testing and provides examples to illustrate the process. It explains how to state the research question and hypotheses, set the decision rule, calculate test statistics, decide if results are significant, and interpret the findings. An example tests if narcissistic individuals look in the mirror more often than others and finds they do based on a test statistic exceeding the critical value. A second example finds no significant difference in recovery time for patients with or without social support after surgery.

5 essential steps for sample size determination in clinical trials slideshare

In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination

PROCEDURE FOR TESTING HYPOTHESIS

This document outlines the process of hypothesis testing. It begins with defining key terms like the null hypothesis (H0), alternative hypothesis (H1), significance level, test statistic, critical value, and decision rule. It then explains the steps involved: 1) setting up H0 and H1, 2) choosing a significance level, 3) calculating the test statistic, 4) finding the critical value, and 5) making a decision by comparing the test statistic and critical value. The overall goal of hypothesis testing is to evaluate claims about a population parameter based on a sample's data.

Sample size calculation

1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.

Hypothesis Testing

Hypothesis Testing

Confidence Intervals: Basic concepts and overview

Confidence Intervals: Basic concepts and overview

Statistical inference

Statistical inference

Probability ppt by Shivansh J.

Probability ppt by Shivansh J.

Chi square tests using SPSS

Chi square tests using SPSS

Poisson distribution

Poisson distribution

Statistical inference concept, procedure of hypothesis testing

Statistical inference concept, procedure of hypothesis testing

Two Proportions

Two Proportions

Estimating a Population Mean

Estimating a Population Mean

Sample size calculation

Sample size calculation

Statistical tests

Statistical tests

SAMPLING THEORY AND TECHNIQUES.pdf

SAMPLING THEORY AND TECHNIQUES.pdf

Z test, f-test,etc

Z test, f-test,etc

Test of-significance : Z test , Chi square test

Test of-significance : Z test , Chi square test

Lecture 5: Interval Estimation

Lecture 5: Interval Estimation

Two Means Independent Samples

Two Means Independent Samples

Hypothesis Testing

Hypothesis Testing

5 essential steps for sample size determination in clinical trials slideshare

5 essential steps for sample size determination in clinical trials slideshare

PROCEDURE FOR TESTING HYPOTHESIS

PROCEDURE FOR TESTING HYPOTHESIS

Sample size calculation

Sample size calculation

Basis of statistical inference

Statistical inference involves using probability concepts to draw conclusions about populations based on samples. It includes point and range estimation to estimate population values, as well as hypothesis testing to test hypotheses about populations. Hypothesis testing involves making a null hypothesis and an alternative hypothesis before collecting sample data. Common hypotheses include claims of no difference or significant differences. Statistical tests like z-tests, t-tests, and chi-square tests are used to either accept or reject the null hypothesis based on the sample data and a significance level, typically 5%. P-values indicate the probability of observing the sample results by chance. Type 1 and type 2 errors can occur when making inferences about hypotheses.

Explanation and statistical inference

1) The document discusses different patterns of explanation for events and facts proposed by Elster, including event-event, event-fact/fact-event, and fact-fact explanations.
2) It also examines methodological individualism and emergence, noting individual actions generate unintended collective consequences and feedback effects.
3) Finally, it cautions against misusing statistical inference and overgeneralizing relationships found in observed cases to unobserved cases, as social phenomena are often unique and non-constant over time.

Completeness

The 7 C's of effective communication are guidelines for choosing content and presentation style adapted to the message's purpose and recipient. The C's are completeness, conciseness, consideration, concreteness, clarity, courtesy, and correctness. Completeness means a communication conveys all necessary facts for the audience, considering their mindset. In business, completeness provides reputation, answers all questions, aids decision-making, and persuades. It gives important details in areas like invoices, production specifications, price tags, and barcodes. A complete communication leaves no unanswered questions for the recipient.

Completeness

This document is a lecture on model theory given by Erik A. Andrejko. It covers topics such as completeness, soundness, elementary submodels, the Tarski-Vaught test, the downward and upward Lowenheim-Skolem-Tarski theorems, definability, categoricity, and complete theories. The document presents definitions, facts, and theorems regarding these topics in model theory.

Probability concept and Probability distribution

The document summarizes key concepts in probability and statistics as they relate to biostatistics and medical research. It discusses basic probability concepts like classical probability, relative frequency probability, and subjective probability. It also covers probability distributions, screening tests, and key metrics like sensitivity and specificity. Specific topics covered include the binomial, Poisson, and normal distributions, conditional probability, joint probability, independence of events, and marginal probability. Examples are provided to demonstrate calculating probabilities from data using concepts like the multiplication rule.

Probability Distributions

A random variable is a rule that assigns a numerical value to each outcome of an experiment. Random variables can be either discrete or continuous. A discrete random variable may assume countable values, while a continuous random variable can assume any value in an interval. The probability distribution of a random variable describes the probabilities of the variable assuming different values. For a continuous random variable, the probability of it assuming a value within an interval is given by the area under the probability density function within that interval.

Basis of statistical inference

Basis of statistical inference

Explanation and statistical inference

Explanation and statistical inference

Completeness

Completeness

Completeness

Completeness

Probability concept and Probability distribution

Probability concept and Probability distribution

Probability Distributions

Probability Distributions

Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...

Web corpora are a cornerstone of modern Language Technology. Corpora built from the web are convenient because their creation is fast and inexpensive. Several studies have been carried out to assess the representativeness of general-purpose web corpora by comparing them to traditional corpora. Less attention has been paid to assess the representativeness of specialized or domain-specific web corpora. In this paper, we focus on the assessment of domain representativeness of web corpora and we claim that it is possible to assess the degree of domainspecificity, or domainhood, of web corpora. We present a case study where we explore the effectiveness of different measures - namely the Mann-Withney-Wilcoxon Test, Kendall correlation coefficient, Kullback– Leibler divergence, log-likelihood and burstiness - to gauge domainhood. Our findings indicate that burstiness is the most suitable measure to single out domain-specific words from a specialized corpus and to allow for the quantification of domainhood.

Towards a Quality Assessment of Web Corpora for Language Technology Applications

In this study, we focus on the creation and evaluation of domain-specific web corpora. To this purpose, we propose a two-step approach, namely the (1) the automatic extraction and evaluation of term seeds from personas and use cases/scenarios; (2) the creation and evaluation of domain-specific web corpora bootstrapped with term seeds automatically extracted in step 1. Results are encouraging and show that: (1) it is possible to create a fairly accurate term extractor for relatively short narratives; (2) it is straightforward to evaluate a quality such as domain-specificity of web corpora using well-established metrics.

A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-

In this study, we put forward two claims: 1) it is possible to design a dynamic and extensible corpus without running the risk of getting into scalability problems; 2) it is possible to devise noise-resistant Language Technology applications without affecting performance. To support our claims, we describe the design, construction and limitations of a very specialized medical web corpus, called eCare_Sv_01, and we present two experiments on lay-specialized text classification. eCare_Sv_01 is a small corpus of web documents written in Swedish. The corpus contains documents about chronic diseases. The sublanguage used in each document has been labelled as "lay" or "specialized" by a lay annotator. The corpus is designed as a flexible text resource, where additional medical documents will be appended over time. Experiments show that the layspecialized labels assigned by the lay annotator are reliably learned by standard classifiers. More specifically, Experiment 1 shows that scalability is not an issue when increasing the size of the datasets to be learned from 156 up to 801 documents. Experiment 2 shows that lay-specialized labels can be learned regardless of the large amount of disturbing factors, such as machine translated documents or low-quality texts, which are numerous in the corpus.

An Exploratory Study on Genre Classification using Readability Features

We present a preliminary study that explores whether text features used for readability assessment are reliable genre-revealing features. We empirically explore the difference between genre and domain. We carry out two sets of experiments with both supervised and unsupervised methods. Findings on the Swedish national corpus (the SUC) show that readability cues are good indicators of genre variation.

Lecture: Semantic Word Clouds

folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology

Lecture: Ontologies and the Semantic Web

Semantic Web, Web 3.0, shared understanding, shared semantic annotation, tree of Porphyry, ontology,wordnet, mesh,rdf, iri, description logics, DLs, Owl, WebProtege, domain-specific,Sparql, tags, ontology learning, classes, relations, axioms, instances, semantics in language technology.

Lecture: Summarization

abstracting, extractive summarization, abstractive summarization, summarization in question answering, single vs. multiple documents, query-focused summarization, snippets, unsupervised content selection, topic signature-based content selection, rouge, recall oriented understudy for gisting evaluation, semantics in language technology,

Relation Extraction

This document discusses various techniques for question answering and relation extraction in natural language processing. It provides an overview of question answering systems and approaches, including examples like START, Ask Jeeves and Siri. It also discusses using search engines for question answering, relation extraction from questions, and common evaluation metrics for question answering systems like accuracy and mean reciprocal rank.

Lecture: Question Answering

IBM's Watson, Apple's Siri, WolframAlpha, factoid questions, complex questions, narrative questions, IR-based approaches, knowledge-based approaches, hybrid approaches, IR-based question answering, answer type taxonomy, passage retrieval,mean reciprocal rank, MRR, semantic analysis in language technology

IE: Named Entity Recognition (NER)

Information Extraction, Named Entity Recognition, NER, text analytics, text mining, e-discovery, unstructured data, structured data, calendaring, standard evaluation per entity, standard evaluation per token, sequence classifier, sequence labeling, word shapes, semantic analysis in language technology

Lecture: Vector Semantics (aka Distributional Semantics)

This document discusses techniques for semantic analysis in natural language processing using distributional semantics or vector space models. It describes how words can be represented as vectors based on their collocational features or surrounding words within a window. It also discusses using bag-of-words features to represent words based on a predefined vocabulary. Finally, it explains Lesk algorithms for word sense disambiguation, which compare the signatures of target words and context words based on dictionary definitions and corpus examples.

Lecture: Word Sense Disambiguation

word sense disambiguation, wsd, thesaurus-based methods, dictionary-based methods, supervised methods, lesk algorithm, michael lesk, simplified lesk, corpus lesk, graph-based methods, word similarity, word relatedness, path-based similarity, information content, surprisal, resnik method, lin method, elesk, extended lesk, semcor, collocational features, bag-of-words features, the window, lexical semantics, computational semantics, semantic analysis in language technology.

Lecture: Word Senses

word senses, lexical semantics, homonymy, polysemy, metonymy, meronymy, antonomy, synonmy, hyponymy, hypernymy, wordnet, mesh, babelnet, lemma, wordform, zeugma test, senseval, selectional restrictions, membership meronymy, part-whole meronymy, semantic analysis, language technology

Sentiment Analysis

This document provides an overview of sentiment analysis and discusses why it is an important area of research in language technology. Sentiment analysis involves detecting positive or negative opinions in text about products, politicians, or other topics. It has many applications, such as determining how consumers feel about a new product or predicting election outcomes based on public sentiment. The document also discusses challenges in modeling affective meaning in language at the lexical level in order to perform tasks like sentiment analysis.

Semantic Role Labeling

Semantic Role Labeling, Thematic Roles, Semantic Roles, PropBank, FrameNet, Selectional Restrictions, Shallow semantics, Shallow semantic representation, Predicate-Argument structure, Computational semantics

Semantics and Computational Semantics

logic and language, formal theories, formal semantics, unification, first-order logic, predicate logic, propositional logic, semantics, computational semantics, meaning representation, connotation, denotation

Lecture 9: Machine Learning in Practice (2)

representation, unbalanced data, multiclass classification, theoretical modelling, real-world implementations, evaluation, holdout estimation, crossvalidation, leave-one-out, bootstrap,

Lecture 8: Machine Learning in Practice (1)

evaluation, t-test, cost-sensitive measures, occam's razor, k-statistic, lift charts, ROC curves, recall-precision curves, loss function, counting the cost, weka

Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio

attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal

Lecture 3b: Decision Trees (1 part)

Greediness, Divide and Conquer, Inductive Bias of the Decision Tree, Loss function, Expected loss, Empirical error
Induction

Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...

Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...

Towards a Quality Assessment of Web Corpora for Language Technology Applications

Towards a Quality Assessment of Web Corpora for Language Technology Applications

A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-

A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-

An Exploratory Study on Genre Classification using Readability Features

An Exploratory Study on Genre Classification using Readability Features

Lecture: Semantic Word Clouds

Lecture: Semantic Word Clouds

Lecture: Ontologies and the Semantic Web

Lecture: Ontologies and the Semantic Web

Lecture: Summarization

Lecture: Summarization

Relation Extraction

Relation Extraction

Lecture: Question Answering

Lecture: Question Answering

IE: Named Entity Recognition (NER)

IE: Named Entity Recognition (NER)

Lecture: Vector Semantics (aka Distributional Semantics)

Lecture: Vector Semantics (aka Distributional Semantics)

Lecture: Word Sense Disambiguation

Lecture: Word Sense Disambiguation

Lecture: Word Senses

Lecture: Word Senses

Sentiment Analysis

Sentiment Analysis

Semantic Role Labeling

Semantic Role Labeling

Semantics and Computational Semantics

Semantics and Computational Semantics

Lecture 9: Machine Learning in Practice (2)

Lecture 9: Machine Learning in Practice (2)

Lecture 8: Machine Learning in Practice (1)

Lecture 8: Machine Learning in Practice (1)

Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio

Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio

Lecture 3b: Decision Trees (1 part)

Lecture 3b: Decision Trees (1 part)

How To Update One2many Field From OnChange of Field in Odoo 17

There can be chances when we need to update a One2many field when we change the value of any other fields in the form view of a record. In Odoo, we can do this. Let’s go with an example.

What is Rescue Session in Odoo 17 POS - Odoo 17 Slides

In this slide, we will discuss the rescue session feature in Odoo 17 Point of Sale (POS). Odoo POS allows us to manage our sales both online and offline. The rescue session helps us recover data in case of internet connectivity issues or accidental session closure.

How to Empty a One2Many Field in Odoo 17

This slide discusses how to delete or clear records in an Odoo 17 one2many field. We'll achieve this by adding a button named "Delete Records." Clicking this button will delete all associated one2many records.

How to Manage Shipping Connectors & Shipping Methods in Odoo 17

Odoo 17 ERP system enables management and storage of various delivery methods for different customers. Timely, undamaged delivery at fair shipping rates leaves a positive impression on clients.

SD_Integrating 21st Century Skills in Classroom-based Assessment.pptx

Matatag Curriculum

matatag curriculum education for Kindergarten

for educational purposes only

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi Book Summary

How to Create a New Article in Knowledge App in Odoo 17

Odoo Knowledge is a multipurpose productivity app that allows internal users to enrich their business knowledge base and provide individually or collaboratively gathered information.

NAEYC Code of Ethical Conduct Resource Book

NAEYC Code of Ethical Conduct Book

Imagination in Computer Science Research

Conducting exciting academic research in Computer Science

What is Packaging of Products in Odoo 17

In Odoo Inventory, packaging is a simple concept of holding multiple units of a specific product in a single package. Each specific packaging must be defined on the individual product form.

Odoo 17 Events - Attendees List Scanning

Use the attendee list QR codes to register attendees quickly. Each attendee will have a QR code, which we can easily scan to register for an event. You will get the attendee list from the “Attendees” menu under “Reporting” menu.

JavaScript Interview Questions PDF By ScholarHat

JavaScript Interview Questions PDF

formative Evaluation By Dr.Kshirsagar R.V

Formative Evaluation Cognitive skill

Benchmarking Sustainability: Neurosciences and AI Tech Research in Macau - Ke...

In this talk we will review recent research work carried out at the University of Saint Joseph and its partners in Macao. The focus of this research is in application of Artificial Intelligence and neuro sensing technology in the development of new ways to engage with brands and consumers from a business and design perspective. In addition we will review how these technologies impact resilience and how the University benchmarks these results against global standards in Sustainable Development.

View Inheritance in Odoo 17 - Odoo 17 Slides

Odoo is a customizable ERP software. In odoo we can do different customizations on functionalities or appearance. There are different view types in odoo like form, tree, kanban and search. It is also possible to change an existing view in odoo; it is called view inheritance. This slide will show how to inherit an existing view in Odoo 17.

1. Importance_of_reducing_postharvest_loss.pptx

Importance_scope, status __postharvest horticulture in nepal

Parent PD Design for Professional Development .docx

Professional Development Papers

modul ajar kelas x bahasa inggris 2024-2025

modul ajar kelas x 2024-2025

FEELINGS AND EMOTIONS INSIDE OUT MOVIE.ppt

Feelings and emotions scenario

How To Update One2many Field From OnChange of Field in Odoo 17

How To Update One2many Field From OnChange of Field in Odoo 17

What is Rescue Session in Odoo 17 POS - Odoo 17 Slides

What is Rescue Session in Odoo 17 POS - Odoo 17 Slides

How to Empty a One2Many Field in Odoo 17

How to Empty a One2Many Field in Odoo 17

How to Manage Shipping Connectors & Shipping Methods in Odoo 17

How to Manage Shipping Connectors & Shipping Methods in Odoo 17

SD_Integrating 21st Century Skills in Classroom-based Assessment.pptx

SD_Integrating 21st Century Skills in Classroom-based Assessment.pptx

matatag curriculum education for Kindergarten

matatag curriculum education for Kindergarten

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi

Power of Ignored Skills: Change the Way You Think and Decide by Manoj Tripathi

How to Create a New Article in Knowledge App in Odoo 17

How to Create a New Article in Knowledge App in Odoo 17

NAEYC Code of Ethical Conduct Resource Book

NAEYC Code of Ethical Conduct Resource Book

Imagination in Computer Science Research

Imagination in Computer Science Research

What is Packaging of Products in Odoo 17

What is Packaging of Products in Odoo 17

Odoo 17 Events - Attendees List Scanning

Odoo 17 Events - Attendees List Scanning

JavaScript Interview Questions PDF By ScholarHat

JavaScript Interview Questions PDF By ScholarHat

formative Evaluation By Dr.Kshirsagar R.V

formative Evaluation By Dr.Kshirsagar R.V

Benchmarking Sustainability: Neurosciences and AI Tech Research in Macau - Ke...

Benchmarking Sustainability: Neurosciences and AI Tech Research in Macau - Ke...

View Inheritance in Odoo 17 - Odoo 17 Slides

View Inheritance in Odoo 17 - Odoo 17 Slides

1. Importance_of_reducing_postharvest_loss.pptx

1. Importance_of_reducing_postharvest_loss.pptx

Parent PD Design for Professional Development .docx

Parent PD Design for Professional Development .docx

modul ajar kelas x bahasa inggris 2024-2025

modul ajar kelas x bahasa inggris 2024-2025

FEELINGS AND EMOTIONS INSIDE OUT MOVIE.ppt

FEELINGS AND EMOTIONS INSIDE OUT MOVIE.ppt

- 1. Machine Learning for Language Technology Lecture 4: Sta,s,cal Inference Marina San,ni Department of Linguis,cs and Philology Uppsala University, Uppsala, Sweden Autumn 2014 Acknowledgement: Thanks to Prof. Joakim Nivre for course design and materials
- 5. Expecta,on
- 6. Variance
- 7. Entropy
- 9. Joint and Condi,onal Probability
- 10. Independence
- 15. Sampling
- 16. Es,ma,on
- 17. Maximum Likelihood Es,ma,on (MLE)
- 18. MLE: Example 1
- 19. MLE: Example 2
- 20. MLE: Ra,onale
- 23. More on Interval Es,ma,on
- 26. The end