markovchain (version 0.8.5)

predictiveDistribution: predictiveDistribution

Description

The function computes the probability of observing a new data set, given a data set

Usage

predictiveDistribution(stringchar, newData, hyperparam = matrix())

Arguments

stringchar

This is the data using which the Bayesian inference is performed.

newData

This is the data whose predictive probability is computed.

hyperparam

This determines the shape of the prior distribution of the parameters. If none is provided, default value of 1 is assigned to each parameter. This must be of size kxk where k is the number of states in the chain and the values should typically be non-negative integers.

Value

The log of the probability is returned.

Details

The underlying method is Bayesian inference. The probability is computed by averaging the likelihood of the new data with respect to the posterior. Since the method assumes conjugate priors, the result can be represented in a closed form (see the vignette for more details), which is what is returned.

References

Inferring Markov Chains: Bayesian Estimation, Model Comparison, Entropy Rate, and Out-of-Class Modeling, Christopher C. Strelioff, James P. Crutchfield, Alfred Hubler, Santa Fe Institute

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

See Also

markovchainFit

Examples

# NOT RUN {
sequence<- c("a", "b", "a", "a", "a", "a", "b", "a", "b", "a", "b", "a", "a", 
             "b", "b", "b", "a")
hyperMatrix<-matrix(c(1, 2, 1, 4), nrow = 2,dimnames=list(c("a","b"),c("a","b")))
predProb <- predictiveDistribution(sequence[1:10], sequence[11:17], hyperparam =hyperMatrix )
hyperMatrix2<-hyperMatrix[c(2,1),c(2,1)]
predProb2 <- predictiveDistribution(sequence[1:10], sequence[11:17], hyperparam =hyperMatrix2 )
predProb2==predProb
# }