Sep 6, 2008 
13 videos 
Bayesian Research Kitchen Workshop (BARK), Grasmere 2008
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.
Bayeswatch
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Introduction to BARK 2008
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Negotiated Interaction : Iterative Inference and Feedback of Intention in HCI
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Multi-task Learning with Gaussian Processes
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Bayesian learning of sparse factor loadings
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Should all Machine Learning be Bayesian? Should all Bayesian models be non-param...
Oct 9, 2008 27918 views
Probabilistic models for ranking and information extraction
Oct 9, 2008 3222 views
Latent Force Models with Gaussian Processes
Oct 9, 2008 4995 views
Variational Model Selection for Sparse Gaussian Process Regression
Oct 9, 2008 6238 views
On the relation between Bayesian inference and certain solvable problems of stoc...
Oct 9, 2008 4690 views
Well-known shortcomings, advantages and computational challenges in Bayesian mod...
Oct 9, 2008 4559 views
Covariance functions and Bayes errors for GP regression on random graphs
Oct 9, 2008 3842 views
The role of mechanistic models in Bayesian inference
Oct 9, 2008 3636 views