mps {hmm.discnp}R Documentation

Most probable states.

Description

Calculates the most probable hidden state underlying each observation.

Usage

mps(y, object = NULL, tpm, Rho, ispd, yval = NULL)

Arguments

y The observations for which the underlying most probable hidden states are required. May be a sequence of observations, or a matrix each column of which constitutes a (replicate) sequence of observations.
object An object describing a fitted hidden Markov model, as returned by hmm(). In order to make any kind of sense, object should bear some reasonable relationship to y.
tpm The transition probability matrix for a hidden Markov model; ignored if object is non-null. Should bear some reasonable relationship to y.
Rho A matrix specifying the probability distributions of the observations for a hidden Markov model; ignored if object is non-null. Should bear some reasonable relationship to y.
ispd The initial state probability distribution for a hidden Markov model; ignored if object is non-null. Should bear some reasonable relationship to y.
yval The set of unique values of the observations; calculated from the observations y if left NULL.

Details

For each t the maximum value of gamma_t(i), i.e. of the (estimated) probability that the state at time t is equal to i, is calculated, and the corresponding index returned. These indices are interpreted as the values of the (most probable) states. I.e. the states are assumed to be 1, 2, ..., K, for some K.

Value

If y is a single observation sequence, then the value is a vector of corresponding most probable states.
If y is a matrix of replicate sequences, then the value is a matrix, the j-th column of which constitutes the vector of most probable states underlying the j-th replicate sequence.

Warning

The sequence of most probable states as calculated by this function will not in general be the most probable sequence of states. It may not even be a possible sequence of states. This function looks at the state probabilities separately for each time t, and not at the states in their sequential context.

To obtain the most probable sequence of states use viterbi().

Author(s)

Rolf Turner r.turner@auckland.ac.nz http://www.math.unb.ca/~rolf

References

Rabiner, L. R., "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE vol. 77, pp. 257 – 286, 1989.

See Also

hmm(), sim.hmm(), viterbi()

Examples

# See the help for sim.hmm() for how to generate y.sim.
## Not run: 
try <- hmm(y.sim,K=2,verb=TRUE)
sss.1 <- mps(y.sim,try)
sss.2 <- mps(y.sim,tpm=P,ispd=c(0.25,0.75),Rho=R) # P and R as in the help
                                                  # for sim.hmm().
# The order of the states has gotten swapped; 3-sss.1[,1] is much
# more similar to sss.2[,1] than is sss.1[,1].
## End(Not run)

[Package hmm.discnp version 0.0-9 Index]