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Bayesian approaches to optimization under bandit feedback have recently become quite popular in the machine learning community. Methods of this type have been found to have not only very good empirica...
We develop a coherent framework for integrative simultaneous analysis of the explorationexploitation and model order selection tradeoffs. We improve over our preceding results on the same subject ...
Because of their multimodality, mixture posterior densities are difficult to sample with standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance the sampling of MCMC in th...
Implementing Bayesian variable selection for linear Gaussian regression models for analysing high dimensional data sets is of current interest in many fields. In order to make such analysis operation...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, where optimality imp...

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