Bayesian Inference with MCMC
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Course overview
- Provider
- Coursera
- Course type
- Free online course
- Level
- Beginner
- Deadline
- Flexible
- Duration
- 15 hours
- Certificate
- Paid Certificate Available
- Course author
- Dr. Srijith Rajamohan
-
1. Markov Chain Monte Carlo algorithms
2. Implementing the above in Python
3. Assess the performance of Bayesian models
Description
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.The instructor for this course will be Dr. Srijith Rajamohan.
