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 |

`data` |
Vector with observation sequence. |

`hmm` |
Object of class |

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## 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]
``` |

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