HMM_EST: MATLAB function to estimate parameters of a 2-state Hidden Markov Model (HMM)
Abstract[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.
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Bibliographic InfoSoftware component provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Software with number M12004.
Programming language: MATLAB
Requires: MATLAB (tested on MATLAB ver. 7.9).
Date of creation: 14 Apr 2012
Date of revision:
Hidden Markov Model (HMM) model; Calibration; Expectation-maximization; Smoothed inferences.; reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
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