A Comprehensive Guide to Bayesian Statistics | Math Online Course by Udemy

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Bayesian Inference, Prior & Posterior Distn, Bayesian Interval Estimation, Bayesian Hypothesis Testing & Decision Theory

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A Comprehensive Guide to Bayesian Statistics

This course is a comprehensive guide to Bayesian Statistics. It includes video explanations along with real life illustrations, examples, numerical problems, take away notes, practice exercise workbooks, quiz, and much more. The course covers the basic theory behind probabilistic and Bayesian modelling, and their applications to common problems in data science, business, and applied sciences. The course is divided into the following sections: Section 1 and 2: These two sections cover the concepts that are crucial to understand the basics of Bayesian Statistics- An overview on Statistical Inference/Inferential StatisticsIntroduction to Bayesian ProbabilityFrequentist/Classical Inference vs Bayesian InferenceBayes Theorem and its application in Bayesian StatisticsReal Life Illustrations of Bayesian StatisticsKey concepts of Prior and Posterior DistributionTypes of PriorSolved numerical problems addressing how to compute the posterior probability distribution for population parameters Conjugate PriorJeffrey’s Non-Informative PriorSection 3: This section covers Interval Estimation in Bayesian Statistics: Confidence Intervals in Frequentist Inference vs Credible Intervals in Bayesian InferenceInterpretation of Confidence Intervals & Credible IntervalsComputing Credible Interval for Posterior MeanSection 4: This section covers Bayesian Hypothesis Testing: Introduction to Bayes FactorInterpretation of Bayes FactorSolved Numerical problems to obtain Bayes factor for two competing hypotheses Section 5: This section caters to Decision Theory in Bayesian Statistics: Basics of Bayesian Decision Theory with examplesDecision Theory Terminology: State/Parameter Space, Action Space, Decision Rule. Loss FunctionReal Life Illustrations of Bayesian Decision TheoryClassification Loss MatrixMinimizing Expected LossDecision making with Frequentist vs Bayesian approachTypes of Loss Functions: Squared Error Loss, Absolute Error Loss, 0-1 LossBayesian Expected LossRisk: Frequentist Risk/Risk Function, Bayes Estimate, and Bayes RiskAdmissibility of Decision RulesProcedures to find Bayes Estimate & Bayes Risk: Normal & Extensive Form of AnalysisSolved numerical problems of computing Bayes Estimate and Bayes Risk for different Loss FunctionsSection 6: This section includes: Bayesian’s Defense & CritiqueApplications of Bayesian Statistics in various fieldsAdditional ResourcesBonus Lecture and a QuizAt the end of the course, you will have a complete understanding of Bayesian concepts from scratch. You will know how to effectively use Bayesian approach and think probabilistically. Enrolling in this course will make it easier for you to score well in your exams or apply Bayesian approach elsewhere. Complete this course, master the principles, and join the queue of top Statistics students all around the world.

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