Course/Event Essentials
Event/Course Start
Event/Course End
Event/Course Format
Online
Live (synchronous)
Primary Event/Course URL
Training Content and Scope
Scientific Domain
Technical Domain
Level of Instruction
Beginner
Intermediate
Sector of the Target Audience
Research and Academia
Industry
HPC Profile of Target Audience
Application Users
Application Developers
Data Scientists
Language of Instruction
Other Information
Organiser
Event/Course Description
When observing data, the key question is: What can I learn from the observation? Bayesian inference treats all parameters of the model as random variables. The main task is to update their distribution as new data is observed. Hence, quantifying uncertainty of the parameter estimation is always part of the task. In this course we will introduce the basic theoretical concepts of Bayesian Statistics and Bayesian inference. We discuss the computational techniques and their implementations, different types of models as well as model selection procedures. We will exercise on the existing datasets use the PyMC3 framework for practicals.
The main topics are:
- Bayes theorem
- Prior and Posterior distributions
- Computational challenges and techniques: MCMC, variational approaches
- Models: mixture models, Gaussian processes, neural networks
- Bayesian model selection: Bayes factor and others
- PyMC3 framework for Bayesian computation
- Running Bayesian models on a Supercomputer