The Bayesian Research Kitchen was held in Grasmere, Ambleside, Lake District, UK from Friday, 5 September 2008 to Sunday, 7 September 2008 at the The Wordsworth Hotel.
This webpage acts as a record of the program and talks given.
To register for the workshop click here.
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.
The workshop is part of the PASCAL FP7 Thematic Programme on "Leveraging Complex Prior Knowledge for Learning".
Workshop FinishedThe workshop is now over. This page is maintained as a record of the workshop with links to slides and videos of the talks.
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.
- 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.
This event is supported by
- Neil D. Lawrence, The University of Manchester
- Joaquin Quinonero Candela, Department of Electrical Engineering and Computer Science, Technical University of Berlin