Bayesian classification of Hidden Markov Models
نویسندگان
چکیده
منابع مشابه
Consistency of Bayesian nonparametric Hidden Markov Models
We are interested in Bayesian nonparametric Hidden Markov Models. More precisely, we are going to prove the consistency of these models under appropriate conditions on the prior distribution and when the number of states of the Markov Chain is finite and known. Our approach is based on exponential forgetting and usual Bayesian consistency techniques.
متن کاملFactorized Asymptotic Bayesian Hidden Markov Models
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture models, FAB for HMMs is derived as an iterative lower bound maximization algorithm...
متن کاملVariational Bayesian Analysis for Hidden Markov Models
The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it appears also to lead to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialised wit...
متن کاملBayesian nonparametric hidden semi-Markov models
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDPHMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can exten...
متن کاملBayesian inference for Hidden Markov Models
Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of a model, as well as the number of regimes. We consider a mixture of normal distributions characterized by different means and variances under eac...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematical and Computer Modelling
سال: 1996
ISSN: 0895-7177
DOI: 10.1016/0895-7177(96)00010-6