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.
Bayesian Statistics: Techniques and Models
This course is part of Bayesian Statistics Specialization
Instructor: Matthew Heiner
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Sponsored by ITC-Infotech
56,139 already enrolled
(486 reviews)
What you'll learn
Efficiently and effectively communicate the results of data analysis.
Use statistical modeling results to draw scientific conclusions.
Extend basic statistical models to account for correlated observations using hierarchical models.
Skills you'll gain
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There are 5 modules in this course
Statistical modeling, Bayesian modeling, Monte Carlo estimation
What's included
11 videos4 readings4 assignments1 discussion prompt
Metropolis-Hastings, Gibbs sampling, assessing convergence
What's included
11 videos7 readings4 assignments
Linear regression, ANOVA, logistic regression, multiple factor ANOVA
What's included
11 videos5 readings5 assignments1 discussion prompt
Poisson regression, hierarchical modeling
What's included
10 videos7 readings4 assignments1 discussion prompt
Peer-reviewed data analysis project
What's included
1 video1 reading1 peer review
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Reviewed on Jul 7, 2018
This is a great course for an introduction to Bayesian Statistics class. Prior knowledge of the use of R can be very helpful. Thanks for such a wonderful course!!!
Reviewed on Aug 31, 2020
One of the best practical math courses present in coursera. Loved the course and will surely look upto the next course eagerly.
Reviewed on Nov 30, 2024
Very good instructor, knowledgeable and thorough, touching the right level of details with big picture in mind, and providing practical guide for hands-on Bayesian data analysis.
Recommended if you're interested in Data Science
Johns Hopkins University
University of Michigan
Johns Hopkins University
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