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Coursera

Bayesian Statistics: Techniques and Models

University of California, Santa Cruz via Coursera

Overview

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.

Syllabus

  • Statistical modeling and Monte Carlo estimation
    • Statistical modeling, Bayesian modeling, Monte Carlo estimation
  • Markov chain Monte Carlo (MCMC)
    • Metropolis-Hastings, Gibbs sampling, assessing convergence
  • Common statistical models
    • Linear regression, ANOVA, logistic regression, multiple factor ANOVA
  • Count data and hierarchical modeling
    • Poisson regression, hierarchical modeling
  • Capstone project
    • Peer-reviewed data analysis project

Taught by

Matthew Heiner

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Reviews

5.0 rating, based on 1 reviews

Start your review of Bayesian Statistics: Techniques and Models

  • Arnaud Dion
    23

    Arnaud Dion completed this course, spending 5 hours a week on it and found the course difficulty to be medium.

    This course follows "Bayesian Statistics: From Concept to Data Analysis". It's mainly about applications of the notions studied during first course. The applications are based on R language. They are interesting and provide insights on what can be done "in real life" situations. The videos are helpful and dynamic. If you are interested to learn about Bayesian Statistics, I recommend this 2 courses. Be aware that you must have a background understanding about statistics before. This is not an introductory course on statistics.

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