posterior: Calculate Posterior Probability for States of HMM
In tileHMM: Hidden Markov Models for ChIP-on-Chip Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

For each state of an HMM the posterior probability that this state produced a given observation is calculated.

1	posterior(data, hmm, log = TRUE)

`data`	Vector with observation sequence.
`hmm`	Object of class `hmm`.
`log`	Logical indicating whether the logarithm of the posterior probability should be returned.

Regardless of the value of log the computation is carried out in log space. If log = FALSE the result is transformed back to linear space before it is returned.

A matrix with as many rows as hmm has states and one column for each entry in data.

Peter Humburg

forward, backward

## create two state HMM with t distributions
state.names <- c("one","two")
transition <- c(0.1, 0.02)
location <- c(1, 2)
scale <- c(1, 1)
df <- c(4, 6)
model <- getHMM(list(a=transition, mu=location, sigma=scale, nu=df),
    state.names)

## obtain observation sequence from model
obs <- sampleSeq(model, 100)

## calculate posterior probability for state "one"
post <- posterior(obs, model, log=FALSE)

## get sequence of individually most likely states
state.seq <- apply(post,2,max)
state.seq <- states(model)[state.seq]