model.ll

Get the log likelihood of an HMM object and check if it is feasible (i.e.,
contains no illegal emissions). Auxiliary function used in
<code>partition.bic</code>.

Provides an implementation of a mixture of hidden Markov models
(HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering
(DBHC) algorithm. The DBHC algorithm is an HMM Clustering
algorithm that finds a mixture of discrete-output HMMs while using
heuristics based on Bayesian Information Criterion (BIC) to search for the
optimal number of HMM states and the optimal number of clusters.

Gabriel Budel

DBHC

Sequence Clustering with Discrete-Output HMMs

model.ll function

<dl><dt>hmm</dt>
<dd>An <code>hmm</code> object (see <code>build_hmm</code>).</dd></dl>

Arguments

Get HMM Log Likelihood — model.ll

<dl>

<dt>hmm</dt>
<dd>An <code>hmm</code> object (see <code>build_hmm</code>).</dd>

</dl>

model.ll: Get HMM Log Likelihood

Description

Usage

Value

Arguments

See Also