R: priorDistribution

priorDistribution {markovchain} | R Documentation

## priorDistribution

### Description

Function to evaluate the prior probability of a transition matrix. It is based on conjugate priors and therefore a Dirichlet distribution is used to model the transitions of each state.

### Usage

    priorDistribution(transMatr, hyperparam = matrix())

### Arguments

`transMatr` | The transition matrix whose probability is the parameter of interest.
`hyperparam` | The hyperparam matrix (optional). If not provided, a default value of 1 is assumed for each and therefore the resulting probability distribution is uniform.

### Details

The states (dimnames) of the transition matrix and the hyperparam may be in any order.

### Value

The log of the probabilities for each state is returned in a numeric vector. Each number in the vector represents the probability (log) of having a probability transition vector as specified in corresponding the row of the transition matrix.

### Note

This function can be used in conjunction with inferHyperparam. For example, if the user has a prior data set and a prior transition matrix, he can infer the hyperparameters using inferHyperparam and then compute the probability of their prior matrix using the inferred hyperparameters with priorDistribution.

### Author(s)

Sai Bhargav Yalamanchi, Giorgio Spedicato

### References

Yalamanchi SB, Spedicato GA (2015). Bayesian Inference of First Order Markov Chains. R package version 0.2.5

### See Also

`predictiveDistribution`, `inferHyperparam`

### Examples

    priorDistribution(matrix(c(0.5, 0.5, 0.5, 0.5),
                      nrow = 2,
                      dimnames = list(c("a", "b"), c("a", "b"))),
                      matrix(c(2, 2, 2, 2),
                      nrow = 2,
                      dimnames = list(c("a", "b"), c("a", "b"))))

[Package markovchain version 0.8.6 Index]