Probability Distance Based Compression of Hidden Markov Models
Hao Wu, Frank Noé · Multiscale Modeling and Simulation · 2010
Large-scale stochastic models are relevant in many different fields such as computational biology, finance, social sciences, communication, and traffic networks. In order to both efficiently simulate and analyze such models and to understand the essential properties of the system, it is desirable to have model reduction techniques that much reduce the dimensionality of the model while at the same time preserving the system's essential dynamical properties. In this paper, a general model reduction technique for the class of discrete space and time hidden Markov models is presented, thereby also including the more special class of discrete Markov chains. The method is illustrated on some model applications.