Motivation
The main aim of this workshop is to allow leading Bayesian researchers in machine learning to get together presenting their latest ideas and discussing future directions.

Themes

• Incorporating Complex Prior Knowledge in Bayesian inference, for example mechanistic models (such as differential equations) or knowledge transfered from other related situations (e.g. hierarchical Dirichlet Processes).
• Model mismatch: the Bayesian lynch pin is that the model is correct, but it rarely is.
• Approximation techniques: how should we do Bayesian inference in practice. Sampling, variational, Laplace or something else?
• Your pet Bayesian issue here.

Visit the Workshop website here.