About
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.
Videos

Negotiated Interaction : Iterative Inference and Feedback of Intention in HCI
Oct 9, 2008
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3555 views

Probabilistic models for ranking and information extraction
Oct 9, 2008
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3209 views

Bayesian learning of sparse factor loadings
Oct 9, 2008
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5294 views

Latent Force Models with Gaussian Processes
Oct 9, 2008
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4972 views

On the relation between Bayesian inference and certain solvable problems of stoc...
Oct 9, 2008
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4672 views

Well-known shortcomings, advantages and computational challenges in Bayesian mod...
Oct 9, 2008
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4521 views

Should all Machine Learning be Bayesian? Should all Bayesian models be non-param...
Oct 9, 2008
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27816 views

Multi-task Learning with Gaussian Processes
Oct 9, 2008
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6299 views

Variational Model Selection for Sparse Gaussian Process Regression
Oct 9, 2008
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6201 views

Bayeswatch
Feb 18, 2024
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8 views

Covariance functions and Bayes errors for GP regression on random graphs
Oct 9, 2008
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3820 views

Introduction to BARK 2008
Oct 9, 2008
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3127 views

The role of mechanistic models in Bayesian inference
Oct 9, 2008
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3609 views