Some topics of Bayesian Inference

Alberto RAMOS, Bryan Zaldivar

The course will consist of 10 hours, 2h per day. The outline is the following:

1. Markov Processes; Markov Chain Monte Carlo; Hybrid (a.k.a. Hamilton) Monte Carlo. 

2. Fundamentals of Bayesian Inference. Laplace Approximation 

3. Variational Inference; Kullback-Leibler divergence. Working example with Tensorflow v2 (python)

4. Inference in function space: Gaussian Processes, Implicit Processes.

5. Approximate Bayesian Computation (ABC). Likelihood-free method. Normalizing flows.


Many of the topics will be accompanied by coding examples.



- Monday, 11:00 - 13:00.  1001-Primera-135 - Nave Exp. Sala de Audiovisuales. Bryan Zaldivar

- Tuesday, 9:30 - 11:30.  1001-Primera-1-1-1 - Paterna. Seminario. Bryan Zaldivar 

Wednesday, 11:00 - 13:00. 1001-Primera-1-1-1 - Paterna. Seminario. Bryan Zaldivar

Thursday, 11:00 - 13:00. 1001-Primera-1-1-1 - Paterna. Seminario. Alberto Ramos

Friday, 11:00 - 13:00. 1001-Primera-1-1-1 - Paterna. Seminario. Bryan Zaldivar

Bryan Zaldivar
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