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.
This course is part of the Bayesian Statistics Specialization
About this Course
Skills you will gain
- Gibbs Sampling
- Bayesian Statistics
- Bayesian Inference
- R Programming
Syllabus - What you will learn from this course
Statistical modeling and Monte Carlo estimation
Markov chain Monte Carlo (MCMC)
Common statistical models
Count data and hierarchical modeling
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- 3 stars2.19%
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- 1 star0.87%
TOP REVIEWS FROM BAYESIAN STATISTICS: TECHNIQUES AND MODELS
Fantastic course that I was able to immediately incorporate into my work. Great mix of theory and hands on coding!
This course is excellent! The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together. I really enjoyed it!!!
terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.
Great course. The instructor provided detailed code examples and clear explanations for model intuitions. The final capstone project is a plus.
About the Bayesian Statistics Specialization
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