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Магистратура 2019/2020

Байесовская статистика

Статус: Курс обязательный (Прикладная политология)
Направление: 41.04.04. Политология
Когда читается: 2-й курс, 1, 2 модуль
Формат изучения: Full time
Преподаватели: Маркварт Кайл Лоус
Прогр. обучения: Прикладная политология
Язык: английский
Кредиты: 4

Программа дисциплины

Аннотация

In this course, students will learn both basic theory of Bayesian statistical analysis as well as important applications that use Bayesian methods. Specifically, the course will focus on using Bayesian methods to analyze survey data and latent concepts, as well as data with a hierarchical structure. Prerequisites: “Methodology and research methods of political science” and “Methods of analyzing heterogeneous data”.
Цель освоения дисциплины

Цель освоения дисциплины

  • Understanding and applying Bayesian methods with important applications; using these methods to produce innovative research outputs.
Результаты освоения дисциплины

Результаты освоения дисциплины

  • Students will be able to design and interpret Bayesian statistical analyses, using prominent software such as JAGS and STAN.
  • Students will learn how to design and implement Bayesian multilevel models and measurement models.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Аn introduction to Bayesian statistics
    This thematic group includes: (1) Introduction to Bayesian statistics, (2) Bayesian mixed methods, (3) Simulation-based Bayesian analysis.
  • Bayesian regression analysis
    This thematic group includes: (1) Implementation and diagnosis, (2) GLMs 1, (3) GLMs 2, (4) Model Choice.
  • Hierarchical models
    This thematic group includes: (1) Introduction, (2) MrP.
  • Latent variable models
    This thematic group includes: (1) Measurement models, (2) Common space models; bridging, (3) Dynamic latent variable models.
Элементы контроля

Элементы контроля

  • неблокирующий Created with Sketch. Participation
  • неблокирующий Created with Sketch. Homework
  • неблокирующий Created with Sketch. Replication project
  • неблокирующий Created with Sketch. Final paper
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (2 модуль)
    0.35 * Final paper + 0.09 * Homework + 0.21 * Participation + 0.35 * Replication project
Список литературы

Список литературы

Рекомендуемая основная литература

  • Humphreys, M., & Jacobs, A. M. (2015). Mixing Methods: A Bayesian Approach. American Political Science Review, (04), 653. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.cup.apsrev.v109y2015i04p653.673.00
  • Pemstein, D., Meserve, S. A., & Melton, J. (2010). Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.FFB194E4
  • Stegmueller, D. (2013). How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches. American Journal of Political Science (John Wiley & Sons, Inc.), 57(3), 748–761. https://doi.org/10.1111/ajps.12001

Рекомендуемая дополнительная литература

  • Fariss, C. J. (2014). Respect for Human Rights has Improved Over Time: Modeling the Changing Standard of Accountability. American Political Science Review, (02), 297. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.cup.apsrev.v108y2014i02p297.318.00
  • Hare, C., Armstrong, D. A., Bakker, R., Carroll, R., & Poole, K. T. (2015). Using Bayesian Aldrich-McKelvey Scaling to Study Citizens’ Ideological Preferences and Perceptions. American Journal of Political Science (John Wiley & Sons, Inc.), 59(3), 759–774. https://doi.org/10.1111/ajps.12151
  • Treier, S., & Jackman, S. (2015). Democracy as a Latent Variable. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EDA89325