Bayesian Statistics: From Concept to Data Analysis

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Департамент социологии

Статус:

Курс по выбору

Когда читается:

2-й курс, 1 модуль

Преподаватель

Фрёлих Кристиан

Course Syllabus

Abstract

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses. The course is a Massive Open Online Course delivered at Coursera platform (https://www.coursera.org/learn/bayesian-statistics). Students are required to attend the course and take an oral examination at HSE for completing the course. The examination is taken after completion of the course during examination weeks. The full syllabus is published at the course website. (https://www.coursera.org/learn/bayesian-statistics). The course doesn’t require special previous knowledge and competences. Only for students of Comparative Social Research programme

Learning Objectives

• Provide students understanding of the basic principles of Bayesian analysis
• Provide students the opportunities which this statistical method offers for social scientists and its limitations

Expected Learning Outcomes

• Apply Bayesian methods to the analysis of real data sets
• Properly report the results of Bayesian analysis in research papers
• To ger knowledge about the philosophy of the Bayesian approach as well as how to implement it for common types of data

Course Contents

• Week 1: Probability and Bayes' Theorem
• Week 2: Statistical Inference
• Week 3: Priors and Models for Discrete Data
• Week 4: Models for Continuous Data

Assessment Elements

• MOOC final results (certificate/another document).
• oral exam
• MOOC final results (certificate/another document).
• oral exam

Interim Assessment

• 2021/2022 1st module
  0.7 * MOOC final results (certificate/another document). + 0.3 * oral exam

Bibliography

Recommended Core Bibliography

• Bolstad, W. M. (2017). Introduction to Bayesian Statistics (Vol. Third edition). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1342637
• Harney, H. L. (2016). Bayesian Inference : Data Evaluation and Decisions (Vol. 2nd ed). Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1301176

Recommended Additional Bibliography

• Donovan, T. M., & Mickey, R. M. (2019). Bayesian Statistics for Beginners : A Step-by-step Approach. Oxford: OUP Oxford. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2139683
• Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian Data Analysis (Vol. Third edition). Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1763244
• Li, K., & Principe, J. C. (2019). Functional Bayesian Filter. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1911.10606