From the jungle to the garden : growing trees for Markov chain Monte Carlo inference in undirected graphical models
In machine-learning, Markov Chain Monte Carlo (MCMC) strategies such as Gibbs sampling are important approximate inference techniques. They use a Markov Chain mechanism to explore and sample the state space of a target distribution. The generated samples are then used to approximate the target di...
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| Language: | English |
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2009
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| Online Access: | http://hdl.handle.net/2429/16689 |