Some topics of Bayesian Inference

1 jun. 2026 11:00 → 5 jun. 2026 13:00 Europe/Madrid

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.

 

****** DAY, TIME, ROOM ***

- Monday, 11:00 - 13:00.  1001-Primera-1-1-1 - Paterna. Seminario.

- Tuesday, 14:30 - 16:30.  1001-Primera-1-1-1 - Paterna. Seminario

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

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

- Friday, 14:30 - 16:30. 1001-Primera-1-1-1 - Paterna. Seminario.