Bayesian inference: a talk with Jim Berger

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Duke University
3.9 (555 ratings) | 49K Students Enrolled
View Syllabus

Skills You'll Learn

Bayesian Statistics, Bayesian Linear Regression, Bayesian Inference, R Programming

Reviews

3.9 (555 ratings)
  • 5 stars
    254 ratings
  • 4 stars
    124 ratings
  • 3 stars
    79 ratings
  • 2 stars
    48 ratings
  • 1 star
    50 ratings
WE

Nov 01, 2016

Very good introduction to Bayesian Statistics. Very interactive with Labs in Rmarkdown. Definitely requires thinking and a good math/analytic background is helpful.

SH

Oct 30, 2017

The course is compact that I've learnt a lot of new concepts in a week of coursework. A good sampler of topics related to Bayesian Statistics.

From the lesson
Perspectives on Bayesian Applications
This week consists of interviews with statisticians on how they use Bayesian statistics in their work, as well as the final project in the course.

Taught By

  • Mine Çetinkaya-Rundel

    Mine Çetinkaya-Rundel

    Associate Professor of the Practice
  • David Banks

    David Banks

    Professor of the Practice
  • Colin Rundel

    Colin Rundel

    Assistant Professor of the Practice
  • Merlise A Clyde

    Merlise A Clyde

    Professor

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