Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models
نویسندگان
چکیده
منابع مشابه
Bayesian 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 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 کاملHidden semi-Markov models
Article history: Received 14 April 2009 Available online 17 November 2009
متن کاملA Spectral Algorithm for Inference in Hidden semi-Markov Models
Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs. Unlike expectation maximization (EM), our approach correctly estimates the probability of given observation sequence based on a set...
متن کاملCollapsed Variational Bayesian Inference for Hidden Markov Models
Approximate inference for Bayesian models is dominated by two approaches, variational Bayesian inference and Markov Chain Monte Carlo. Both approaches have their own advantages and disadvantages, and they can complement each other. Recently researchers have proposed collapsed variational Bayesian inference to combine the advantages of both. Such inference methods have been successful in several...
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: Journal of Computational and Graphical Statistics
سال: 2019
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2019.1654880