Modeling and analysis of stochastic systems
Modeling and analysis of stochastic systems
A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
IEEE Transactions on Information Theory
Baum's forward-backward algorithm revisited
Pattern Recognition Letters
Exploring the state sequence space for hidden Markov and semi-Markov chains
Computational Statistics & Data Analysis
On sovereign credit migration: A study of alternative estimators and rating dynamics
Computational Statistics & Data Analysis
PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining
Expert Systems with Applications: An International Journal
Hidden Markov models with arbitrary state dwell-time distributions
Computational Statistics & Data Analysis
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Models that combine Markovian states with implicit geometric state occupancy distributions and semi-Markovian states with explicit state occupancy distributions, are investigated. This type of model retains the flexibility of hidden semi-Markov chains for the modeling of short or medium size homogeneous zones along sequences but also enables the modeling of long zones with Markovian states. The forward-backward algorithm, which in particular enables to implement efficiently the E-step of the EM algorithm, and the Viterbi algorithm for the restoration of the most likely state sequence are derived. It is also shown that macro-states, i.e. series-parallel networks of states with common observation distribution, are not a valid alternative to semi-Markovian states but may be useful at a more macroscopic level to combine Markovian states with semi-Markovian states. This statistical modeling approach is illustrated by the analysis of branching and flowering patterns in plants.