ll_un_normalized_hmm_gm: ll_un_normalized_hmm_gm
In HMMmlselect: Determine the Number of States in Hidden Markov Models via Marginal Likelihood
Description
Usage
Arguments
Details
Value
References
View source: R/HMMorderselectionfacility.R
Description
Auxiliary function that computes the unnormalized posterior density of HMM and Gaussian mixture.
Usage
1
ll_un_normalized_hmm_gm(paras, yobs, bool_hmm, priors)
Arguments
paras
The parameter for the HMM.
yobs
The observed data.
bool_hmm
1 if it's HMM. 0 if it's Gaussian mixture.
priors
Prior distribution.
Details
See Manual.pdf in "inst/extdata" folder.
Value
It returns the unnormalized posterior density of HMM and Gaussian mixture.
References
Yang Chen, Cheng-Der Fuh, Chu-Lan Kao, and Samuel Kou (2019+) "Determine the number of states
in hidden markov models via marginal likelihood." Submitted.
HMMmlselect documentation built on May 3, 2020, 9:05 a.m.
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Description Usage Arguments Details Value References
View source: R/HMMorderselectionfacility.R
Description
Auxiliary function that computes the unnormalized posterior density of HMM and Gaussian mixture.
Usage
1 | ll_un_normalized_hmm_gm(paras, yobs, bool_hmm, priors)
|
Arguments
paras |
The parameter for the HMM. |
yobs |
The observed data. |
bool_hmm |
1 if it's HMM. 0 if it's Gaussian mixture. |
priors |
Prior distribution. |
Details
See Manual.pdf in "inst/extdata" folder.
Value
It returns the unnormalized posterior density of HMM and Gaussian mixture.
References
Yang Chen, Cheng-Der Fuh, Chu-Lan Kao, and Samuel Kou (2019+) "Determine the number of states in hidden markov models via marginal likelihood." Submitted.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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