Sampling and Filtering with Markov Chains
马尔可夫链采样与滤波
A continuous-time Markov chain rate change formula for simulation, model selection, filtering and theory is proven. It is used to develop Markov chain importance sampling, rejection sampling, branching particle filtering algorithms and filtering equations akin to the Duncan-Mortensen-Zakai equation and the Fujisaki-Kallianpur-Kunita equation but for Markov signals with general continuous-time Markov chain observations. A direct method of solving these filtering equations is…