Modeling of multivariate time series using hidden markov models
Modeling of multivariate time series using hidden markov models
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Scientific Programming - Hidden Markov Models
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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The Double Chain Markov Model (DCMM) is used to model an observable process $Y = \{Y_{t}\}_{t=1}^{T}$ as a Markov chain with transition matrix, $P_{x_{t}}$ , dependent on the value of an unobservable (hidden) Markov chain $\{X_{t}\}_{t=1}^{T}$ . We present and justify an efficient algorithm for sampling from the posterior distribution associated with the DCMM, when the observable process Y consists of independent vectors of (possibly) different lengths. Convergence of the Gibbs sampler, used to simulate the posterior density, is improved by adding a random permutation step. Simulation studies are included to illustrate the method. The problem that motivated our model is presented at the end. It is an application to real data, consisting of the credit rating dynamics of a portfolio of financial companies where the (unobserved) hidden process is the state of the broader economy.