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The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final 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.
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1 - 4 von 4 Bewertungen für Introduction to PyMC3 for Bayesian Modeling and Inference
von Flavio L
•24. Jan. 2022
amazing, nice material, well explained
von Jack R
•7. Mai 2022
Will teach you a lot about Bayesian statistics, but often times the code is not well suited for beginners. Especially the final project. They give the code to do it, but a standard logistic regression problem would have been better suited rater than a differential equations model with various classes.
von Rafael B
•10. Aug. 2021
Coding evaluations don't exist as promised, the more advanced content regarding MCMC diagnosis and applicate bayesian inference is just thrown away without good explanations
von Wang X
•5. Jan. 2022
Can not reproduce the results from the codes, and no helps gotten either from the forums nor reply from instructors (through email).
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