Bayesian Research Kitchen Workshop (BARK), Grasmere 2008

Bayesian Research Kitchen Workshop (BARK), Grasmere 2008

12 Lectures · Sep 6, 2008

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.

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01:00:37

Should all Machine Learning be Bayesian? Should all Bayesian models be non-param...

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Introduction to BARK 2008

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The role of mechanistic models in Bayesian inference

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Well-known shortcomings, advantages and computational challenges in Bayesian mod...

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Variational Model Selection for Sparse Gaussian Process Regression

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Negotiated Interaction : Iterative Inference and Feedback of Intention in HCI

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