Nonparametric methods of inference for finite-state, inhomogeneous Markov processes
In some inferential problems involving Markov process data, the inhomogeneity of the process is of central interest. One example is of a binary time series of data on the presence or absence of a species at a particular site over time. Here the two states correspond to 'presence' or 'absence,' respectively, of the species, and the main topic of interest is temporal variation in the process. In principle this variation can be modelled parametrically, but in the absence of information about the...[Show more]
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