The course will consist of 10 hours, 2h per day. 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. Approximate Bayesian Computation (ABC). Likelihood-free method. Normalizing flows.
5. Markov Processes; Markov Chain Monte Carlo; Hybrid (a.k.a. Hamilton) Monte Carlo.
Many of the topics will be accompanied by coding examples.
****** DAY, TIME, ROOM ***
- 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.
- Monday 8th, 11:00 - 13:00. Sala de Audiovisuales