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Opiniones y comentarios de aprendices correspondientes a Bayesian Inference with MCMC por parte de Databricks

3.0
estrellas
10 calificaciones
2 reseña

Acerca del Curso

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....
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1 - 2 de 2 revisiones para Bayesian Inference with MCMC

por GBM

29 de dic. de 2021

I had to look in other materials to understand most of the algorithms, the way it was presented just reading the material doesn't help much. As a result, I ended up preferring to just read the material rather than watch it. So, I think it could be a more dynamic class to improve didactics.

por Ross

7 de mar. de 2022

T​his course is inappropriately calibrated. At best, it is a fair refresher for someone who has already taken a graduate level course in Bayesian statistics. The coding assignments are do-able, but the course content does not include an example solution to the ungraded coding homework assigments. The lectures are fair to poor, with notes that neither visually convey the intuition of each method nor the mathematical details. In summary, the class is an ok 8 hour refresher for someone who already knows Bayesian statistics and MCMC.