A Comprehensive Guide to Bayesian Statistics

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 Statistics

• Introduction to Bayesian Probability

• Frequentist/Classical Inference vs Bayesian Inference

• Bayes Theorem and its application in Bayesian Statistics

• Real Life Illustrations of Bayesian Statistics

• Key concepts of Prior and Posterior Distribution

• Types of Prior

• Solved numerical problems addressing how to compute the posterior probability distribution for population parameters

• Conjugate Prior

• Jeffrey's Non-Informative Prior

Section 3: This section covers Interval Estimation in Bayesian Statistics:

• Confidence Intervals in Frequentist Inference vs Credible Intervals in Bayesian Inference

• Interpretation of Confidence Intervals & Credible Intervals

• Computing Credible Interval for Posterior Mean

Section 4: This section covers Bayesian Hypothesis Testing:

• Introduction to Bayes Factor

• Interpretation of Bayes Factor

• Solved 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 examples

• Decision Theory Terminology: State/Parameter Space, Action Space, Decision Rule. Loss Function

• Real Life Illustrations of Bayesian Decision Theory

• Classification Loss Matrix

• Minimizing Expected Loss

• Decision making with Frequentist vs Bayesian approach

• Types of Loss Functions: Squared Error Loss, Absolute Error Loss, 0-1 Loss

• Bayesian Expected Loss

• Risk : Frequentist Risk/Risk Function, Bayes Estimate, and Bayes Risk

• Admissibility of Decision Rules

• Procedures to find Bayes Estimate & Bayes Risk: Normal & Extensive Form of Analysis

• Solved numerical problems of computing Bayes Estimate and Bayes Risk for different Loss Functions

Section 6: This section includes:

• Bayesian's Defense & Critique

• Applications of Bayesian Statistics in various fields

• Additional Resources

• Bonus Lecture and a Quiz

At 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.

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

Url: View Details

What you will learn

• An Overview on Statistical Inference
• Frequentist vs Bayesian approach to Statistical Inference
• Clearly understand Bayes Theorem and its application in Bayesian Statistics

Rating: 4.45

Level: Intermediate Level

Duration: 3 hours

Instructor: Twinkle Sethi