A flat Dirichlet process switching model for Bayesian estimation of hybrid systems | Zendy

Hao Wu | Zendy; Frank Noé | Zendy

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A flat Dirichlet process switching model for Bayesian estimation of hybrid systems

Author(s) -

Hao Wu,

Frank Noé

Publication year - 2011

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2011.04.150

Subject(s) - dirichlet process , computer science , hierarchical dirichlet process , hybrid system , dirichlet distribution , markov chain monte carlo , process (computing) , mode (computer interface) , bayesian probability , markov chain , jump , algorithm , artificial intelligence , machine learning , mathematics , latent dirichlet allocation , topic model , mathematical analysis , boundary value problem , operating system , physics , quantum mechanics

Hybrid systems are often used to describe many complex dynamic phenomena by combining multiple modes of dynamics into whole systems. In this paper, we present a flat Dirichlet process switching (FDPS) model that defines a prior on mode switching dynamics of hybrid systems. Compared with the classical Markovian jump system (MJS) models, the FDPS model is nonparametric and can be applied to the hybrid systems with an unbounded number of potential modes. On the other hand, the probability structure of the new model is simpler and more flexible than the recently proposed hierarchical Dirichlet process (HDP) based MJS. Furthermore, we develop a Markov chain Monte Carlo (MCMC) method for estimating the states of hybrid systems with FDPS prior. And the numerical simulations of a hybrid system in different conditions are employed to show the effectiveness of the proposed approach

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