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Abstract: Many real-world systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition, target tracking, and monitoring of physical systems. The task is usually to perform probabilistic inference, i.e., infer the hidden state of the system given some noisy observations. For example, we can ask what is the probability that a certain word was pronounced given the readings of our microphone,... (Update)
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BibTeX entry: (Update)
Uri Lerner. Hybrid Bayesian Networks for Reasoning About Complex Systems. PhD thesis, Stanford University, October 2002. http://citeseer.ist.psu.edu/lerner02hybrid.html More
@misc{ lerner02hybrid,
author = "U. Lerner",
title = "Hybrid Bayesian Networks for Reasoning About Complex Systems",
text = "Uri Lerner. Hybrid Bayesian Networks for Reasoning About Complex Systems.
PhD thesis, Stanford University, October 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/lerner02hybrid.html" }
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