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Course Syllabus

Master 2024/2025

Bayesian Data Analysis

Type: Elective course (Data Analytics and Social Statistics)
Area of studies: Applied Mathematics and Informatics
Delivered by: International Laboratory for Applied Network Research
Where: Faculty of Social Sciences
When: 2 year, 3 module
Mode of studies: offline
Open to: students of one campus
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 3
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

This course introduces the basic theoretical and applied principles of Bayesian statistical analysis in a manner geared toward students in the social sciences. The Bayesian paradigm is particularly useful for the type of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed.
Learning Objectives

Learning Objectives

  • The goal of the course is ensure that students understand topics and principles of Bayesian approach to analyzing social science data.
Expected Learning Outcomes

Expected Learning Outcomes

  • Have the skill to work with statistical software, required to analyze the data.
  • Be able to criticize constructively and determine existing issues with applied linear models in published work
  • Have the skill to meaningfully develop an appropriate model for the research question.
  • Know innovative, effective methods for presenting the results from statistical investigations of empirical data.
  • Be able to develop and/or foster critical reviewing skills of published empirical research using Bayesian methods.
  • Be able to estimate and interpret Bayesian models from an applied perspective.
  • Be able to work with major Bayesian estimation programs, especially R and SAS, so that they can use them and interpret their output.
  • Have an understanding of advanced methods of Bayesian analysis.
  • Know Bayesian forms of the standard statistical models taught in regression and MLE courses (i.e., normal, logit/probit, Poisson, etc.)as well as a variety of measurement and multilevel models.
  • Know strengths of the Bayesian approach for social science data and the philosophical differences between Bayesian and frequentist analyses.
  • Know theoretical underpinnings of Bayesian modeling and primary estimation algorithms.
Course Contents

Course Contents

  • Introduction, Background, and Basics of Bayesian Inference
  • Review of prior topics
  • Priors
  • Sampling methods and introduction to Bayesian analysis in R
  • Convergence diagnostics
  • The Bayesian linear model
  • Missing data
  • Dichotomous variable models and IRT models
  • Measurement models and identification
  • Introduction to multilevel models
  • Limitations of Bayesian statistics
Assessment Elements

Assessment Elements

  • non-blocking Mid-term take-home exam
  • non-blocking In-class labs and homeworks
  • non-blocking Final Exam
Interim Assessment

Interim Assessment

  • 2024/2025 3rd module
    0.5 * Final Exam + 0.2 * In-class labs and homeworks + 0.3 * Mid-term take-home exam
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
  • Borek Puza. (2015). Bayesian Methods for Statistical Analysis. Netherlands, Europe: ANU Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C43E3A69
  • Hahn, E. D. (2014). Bayesian Methods for Management and Business : Pragmatic Solutions for Real Problems. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=830682
  • Hjort, N. L. (2010). Bayesian Nonparametrics. Cambridge, UK: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=312516
  • Rossi, P. E. (2014). Bayesian Non- and Semi-parametric Methods and Applications. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=681619

Recommended Additional Bibliography

  • Congdon, P. (2014). Applied Bayesian Modelling (Vol. Second edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=784135
  • Yang, X.-S., & Jeliazkov, I. (2014). Bayesian Inference in the Social Sciences. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=888972

Authors

  • Павлова Ирина Анатольевна
  • Klimov Ivan Aleksandrovich