The conjugate prior distribution for the parameters of a homogeneous Markov chain. The rows in the transition probability matrix modeled with independent Dirichlet priors. The distribution of the initial state is modeled with its own independent Dirichlet prior.
MarkovPrior(prior.transition.counts = NULL,
prior.initial.state.counts = NULL,
state.space.size = NULL,
uniform.prior.value = 1)
A matrix of the same dimension as the
transition probability matrix being modeled. Entry (i, j) represents
the "prior count" of transitions from state i to state j.
A vector of positive numbers representing prior counts of initial states.
If both prior.transition.counts and
prior.initial.state.counts are missing, then they will be filled
with an object of dimension state.space.size where all entries are
set to uniform.prior.value.
The default value to use for entries of
prior.transition.counts and
prior.initial.state.counts, when they are not supplied by the
user.
Steven L. Scott steve.the.bayesian@gmail.com
Gelman, Carlin, Stern, Rubin (2003), "Bayesian Data Analysis", Chapman and Hall.