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Trans-dimensional MCMC for Bayesian policy learning

Abstract:

A recently proposed formulation of the stochastic planning and control problem as one of parameter estimation for suitable artificial statistical models has led to the adoption of inference algorithms for this notoriously hard problem. At the algorithmic level, the focus has been on developing Expectation-Maximization (EM) algorithms. In this paper, we begin by making the crucial observation that the stochastic control problem can be reinterpreted as one of trans-dimensional inference. With t...

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Authors


Hoffman, M More by this author
De Freitas, N More by this author
Journal:
Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
Publication date:
2009
URN:
uuid:9059b5b6-87d3-448b-a8da-a59f2f3d104d
Source identifiers:
321647
Local pid:
pubs:321647
Language:
English

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