Schools

Some topics of Bayesian Inference

by Alberto Ramos, Bryan Zaldivar

Europe/Madrid
Description

The course will consist of 10 hours, 2h per day, from 11:00 to 13:00. The outline is the following:

1. Fundamentals of Bayesian Inference. Laplace Approximation 

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

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

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

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-135 - Nave Exp. Sala de Audiovisuales

- Tuesday, 9:30 - 11:30.  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, 11:00 - 13:00. 1001-Primera-1-1-1 - Paterna. Seminarioles