Estimation of Viterbi path in Bayesian hidden Markov models
| Autor: | Alexey Koloydenko, Kristi Kuljus, Dario Gasbarra, Jüri Lember |
|---|---|
| Přispěvatelé: | University of Helsinki, Department of Mathematics and Statistics, Survival and event history analysis |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Iterative method Computer science Posterior probability CHAIN MONTE-CARLO MAP path 02 engineering and technology Viterbi algorithm 01 natural sciences Statistics - Computation Simulated annealing 010104 statistics & probability symbols.namesake Bayes' theorem Segmentation 111 Mathematics 0202 electrical engineering electronic engineering information engineering Maximum a posteriori estimation HMM 0101 mathematics Hidden Markov model Computation (stat.CO) Markov chain Monte Carlo Bayes inference Statistics::Computation ComputingMethodologies_PATTERNRECOGNITION EM Path (graph theory) symbols 020201 artificial intelligence & image processing TUTORIAL Variational Bayes CLASSIFICATION EM ALGORITHM Algorithm |
| Popis: | The article studies different methods for estimating the Viterbi path in the Bayesian framework. The Viterbi path is an estimate of the underlying state path in hidden Markov models (HMMs), which has a maximum joint posterior probability. Hence it is also called the maximum a posteriori (MAP) path. For an HMM with given parameters, the Viterbi path can be easily found with the Viterbi algorithm. In the Bayesian framework the Viterbi algorithm is not applicable and several iterative methods can be used instead. We introduce a new EM-type algorithm for finding the MAP path and compare it with various other methods for finding the MAP path, including the variational Bayes approach and MCMC methods. Examples with simulated data are used to compare the performance of the methods. The main focus is on non-stochastic iterative methods and our results show that the best of those methods work as well or better than the best MCMC methods. Our results demonstrate that when the primary goal is segmentation, then it is more reasonable to perform segmentation directly by considering the transition and emission parameters as nuisance parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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