multivariate poisson hidden markov models for analysis of spatial ...

CHAPTER 6
RESULTS OF MULTIVARIATE POISSON FINITE MIXTURE MODELS AND
MULTIVARIATE POISSON HIDDEN MARKOV MODELS
6.1 Introduction
In this chapter, the results of the multivariate Poisson finite mixture (independent) and
the multivariate Poisson hidden Markov models are discussed. The preliminary analysis
is presented in sections 6.2 and 6.3. The methodology explained in Chapter 5 is used to
calculate the posterior probabilities and to estimate the corresponding parameters. The
results of the empirical analysis are presented in section 6.4. A comparison of different
model specifications is given in section 6.5.
6.2 Exploratory data analysis
The histogram of species counts for each of the variable and the basic statistics,
including the mean and the variance per variable, are illustrated in Figure 6.1 and Table
6.1 respectively. In fact, several important conclusions can be made from these
histograms and the basic statistics. First of all, it can be seen from the histograms that
the data were discrete integer values (i.e. count data) that can be assumed to model by a
Poisson distribution. It is generally accepted in the literature (Johnson et al., 2005) that
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