Bayesian inference for Markov processes with diffusion and discrete components
含扩散与离散分量的马尔可夫过程的贝叶斯推断
Data arising in certain radio‐tracking experiments consist of both a continuous spatial component and a discrete component related to behaviour. This leads naturally to stochastic models with a state space which is a product of continuous and discrete components. We consider a class of such models in continuous time, which can be thought of as diffusions in random environments. They are related to switching diffusion or hidden Markov models, but observations are made on both…