HMM_EST: MATLAB function to estimate parameters of a 2-state Hidden Markov Model (HMM)
[KSI_TT,PARAM,P]=HMM_EST(DATA,MODEL) returns smoothed inferences KSI_TT, estimated parameters PARAM and transition matrix P of a 2-state Hidden Markov Model (HMM) with distributions specified by MODEL: (i) Gaussian in both regimes (MODEL='G-G '), (ii) Lognormal in both regimes (MODEL='LN-LN'), or (iii) Gaussian in the first regime and lognormal in the second (MODEL='G-LN ') fitted to time series DATA. The first column (KSI_TT) or row (PARAM, P) contains results for the first regime and the second column/row for the second regime.[KSI_TT,PARAM,P,KSI_T1T_10,LOGL]=HMM_EST(DATA,MODEL) additionally returns probabilities KSI_T1T_10 classifying the first observation to one of the regimes and log-likelihood LOGL of the fitted model.
|Requires:||MATLAB (tested on MATLAB ver. 7.9).|
|Date of creation:||14 Apr 2012|
|Date of revision:|
|Contact details of provider:|| Postal: Wybrzeze Wyspianskiego 27, 50-370 Wroclaw|
Web page: http://prac.im.pwr.wroc.pl/~hugo
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:wuu:hscode:m12004. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Rafal Weron)
If references are entirely missing, you can add them using this form.