Байесовские методы анализа данных

2024/2025

Статус: Маго-лего

Кто читает: Международная лаборатория прикладного сетевого анализа

Когда читается: 3 модуль

Охват аудитории: для своего кампуса

Язык: русский

Кредиты: 3

Полная версия программы учебной дисциплины

Аннотация

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.

Цель освоения дисциплины

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

Планируемые результаты обучения

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.