Towards Practical Bayesian Parameter and State Estimation

29 Mar 2016Yusuf Bugra ErolYi WuLei LiStuart Russell

Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet---an efficient and general online inference algorithm for such problems---remains elusive, forcing users to write special-purpose code for each application... (read more)

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