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