1 research outputs found
Towards Practical Bayesian Parameter and State Estimation
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. We propose a novel blackbox algorithm -- a hybrid of particle
filtering for state variables and assumed density filtering for parameter
variables. It has following advantages: (a) it is efficient due to its online
nature, and (b) it is applicable to both discrete and continuous parameter
spaces . On a variety of toy and real models, our system is able to generate
more accurate results within a fixed computation budget. This preliminary
evidence indicates that the proposed approach is likely to be of practical use