MCMC With Disconnected State Spaces
نویسندگان
چکیده
Bayes Nets simplify probabilistic models, making it easy to work with these models. Unfortunately, sometimes people devise models that are too complicated to allow calculation of exact probabilities, so they instead use approximate inference, such as Markov Chain Monte Carlo (MCMC). However, MCMC can fail if the Bayes Net has zero-probability states that “disconnect” the state space. In this paper, we attempt to modify MCMC to handle such nets robustly while not losing sight of efficiency.
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