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Regular version of the site
Bachelor 2019/2020

Bayesian Statistics: From Concept to Data Analysis

Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Linguistics
When: 3 year, 3 module
Mode of studies: с онлайн-курсом
Language: English
ECTS credits: 3

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. University of California, Santa Cruz: https://www.coursera.org/learn/bayesian-statistics
Learning Objectives

Learning Objectives

  • to learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data
  • to compare the Bayesian approach to the more commonly-taught Frequentist approach
Expected Learning Outcomes

Expected Learning Outcomes

  • understands the concepts of the Bayesian approach
  • understands the key differences between Bayesian and Frequentist approaches
  • is able to do basic data analyses
Course Contents

Course Contents

  • Probability and Bayes' Theorem
  • Statistical Inference
  • Priors and Models for Discrete Data
  • Models for Continuous Data
Assessment Elements

Assessment Elements

  • non-blocking online course
  • non-blocking discussion with a HSE instructor
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * discussion with a HSE instructor + 0.7 * online course
Bibliography

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

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