Bayesian Research Kitchen Workshop

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. The workshop will provide a forum to discuss Bayesian inference in machine learning. A particular focus will be on how Bayesian inference can be used to encode prior knowledge.

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.