| Interface | Description |
|---|---|
| HMMDistribution |
This is the conditional distribution of the observations in each state
(possibly differently parameterized) of a mixture hidden Markov model.
|
| Class | Description |
|---|---|
| BetaDistribution |
The HMM states use the Beta distribution to model the observations.
|
| BetaDistribution.Lambda |
the Beta distribution parameters
|
| BinomialDistribution |
The HMM states use the Binomial distribution to model the observations.
|
| BinomialDistribution.Lambda |
the Binomial distribution parameters
|
| ExponentialDistribution |
The HMM states use the Exponential distribution to model the observations.
|
| GammaDistribution |
The HMM states use the Gamma distribution to model the observations.
|
| GammaDistribution.Lambda |
the Gamma distribution parameters
|
| LogNormalDistribution |
The HMM states use the Log-Normal distribution to model the observations.
|
| LogNormalDistribution.Lambda |
the Log-Normal distribution parameters
|
| NormalDistribution |
The HMM states use the Normal distribution to model the observations.
|
| NormalDistribution.Lambda |
the Normal distribution parameters
|
| PoissonDistribution |
The HMM states use the Poisson distribution to model the observations.
|