A workshop introducing basic Bayesian modelling, using practical examples and probabilistic programming.
- Understanding the Bayesian methodology, and the foundations of
approximate bayesian inference.
- First steps in probabilistic programming in pyro.
- Getting to know the model based machine learning approach:
- Learn to make the right assumptions.
- Criticise your own model.
- Getting to know many model types and comparing their performance.
- Applying these models to a wide range of areas.
- Combining probabilistic modelling with deep learning.
Structure of the workshop
- Introduction to Bayesian methods and probabilistic programming languages.
- Inference with discrete variables: Who has the right skill set for your job offering?
- Inference with Markov chain Monte Carlo methods: Marriages, divorces and causality.
- The Lotka-Volterra predator-prey model. Advantages and necessity of
hybrid approaches combining deterministic and statistical models.
- Stochastic variational inference and the mean field assumption.
- Clustering images with mixture models and latent Dirichlet allocation like models.
- Understanding the development of asthma through Markov chains.
- Become a wine connoisseur with Bayesian neural networks.
- Modeling faces through the ages with variational autoencoders.