initHMM {HMM}R Documentation

Initialisation of HMM's

Description

This function initialises a general discrete time and discrete space Hidden Markov Model (HMM). A HMM consists of an alphabet of states and emission symbols. A HMM assumes that the states are hidden from the observer, while only the emissions of the states are observable. The HMM is designed to make inference on the states through the observation of emissions. The stochastics of the HMM is fully described by the initial starting probabilities of the states, the transition probabilities between states and the emission probabilities of the states.

Usage

initHMM(States, Symbols, startProbs=NULL, transProbs=NULL, emissionProbs=NULL)

Arguments

States Vector with the names of the states.
Symbols Vector with the names of the symbols.
startProbs Vector with the starting probabilities of the states.
transProbs Stochastic matrix containing the transition probabilities between the states.
emissionProbs Stochastic matrix containing the emission probabilities of the states.

Format

Dimension and Format of the Arguments.

States
Vector of strings.
Symbols
Vector of strings.
startProbs
Vector with the starting probabilities of the states. The entries must sum to 1.
transProbs
transProbs is a (number of states)x(number of states)-sized matrix, which contains the transition probabilities between states. The entry transProbs[X,Y] gives the probability of a transition from state X to state Y. The rows of the matrix must sum to 1.
emissionProbs
emissionProbs is a (number of states)x(number of states)-sized matrix, which contains the emission probabilities of the states. The entry emissionProbs[X,e] gives the probability of emission e from state X. The rows of the matrix must sum to 1.

Details

In transProbs and emissionProbs NA's can be used in order to forbid specific transitions and emissions. This might be useful for Viterbi training or the Baum-Welch algorithm when using pseudocounts.

Value

The function initHMM returns a HMM that consists of a list of 5 elements:

States Vector with the names of the states.
Symbols Vector with the names of the symbols.
startProbs Annotated vector with the starting probabilities of the states.
transProbs Annotated matrix containing the transition probabilities between the states.
emissionProbs Annotated matrix containing the emission probabilities of the states.

Author(s)

Lin Himmelmann <hmm@linhi.com>, Scientific Software Development

References

For an introduction in the HMM-literature see for example:

See Also

See simHMM to simulate a path of states and observations from a Hidden Markov Model.

Examples

# Initialise HMM nr.1
initHMM(c("X","Y"), c("a","b","c"))
# Initialise HMM nr.2
initHMM(c("X","Y"), c("a","b"), c(.3,.7), matrix(c(.9,.1,.1,.9),2),
        matrix(c(.3,.7,.7,.3),2))

[Package HMM version 1.0 Index]