markov.prior: Prior for a Markov chain

In Boom: Bayesian Object Oriented Modeling

Prior for a Markov chain

Description

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.

Usage

MarkovPrior(prior.transition.counts = NULL,
            prior.initial.state.counts = NULL,
            state.space.size = NULL,
            uniform.prior.value = 1)

Arguments

prior.transition.counts	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.
prior.initial.state.counts	A vector of positive numbers representing prior counts of initial states.
state.space.size	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.
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.

Author(s)

Steven L. Scott [e-mail]

References

Gelman, Carlin, Stern, Rubin (2003), "Bayesian Data Analysis", Chapman and Hall.

Boom documentation built on May 29, 2024, 5:08 a.m.