Towards Practical Bayesian Parameter and State Estimation
This addresses a core bottleneck in probabilistic modeling for practitioners who need efficient online inference without custom coding, though it appears incremental as a hybrid of existing methods.
The paper tackles the problem of efficient online joint state and parameter estimation in dynamic Bayesian networks, proposing a hybrid algorithm that combines particle filtering for states and assumed density filtering for parameters. It shows that on various models, the system generates more accurate results within a fixed computation budget.
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.