Algorithms for Hidden Markov Models With Imprecisely Specified Parameters

D. D. Mauá, C. P. de Campos, A. Antonucci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Hidden Markov models (HMMs) are widely used models for sequential data. As with other probabilistic graphical models, they require the specification of precise probability values, which can be too restrictive for some domains, especially when data are scarce or costly to acquire. We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions. Our models have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. Efficient inference algorithms are developed to address standard HMM usage such as the computation of likelihoods and most probable explanations. Experiments with real data show that the use of imprecise probabilities leads to more reliable inferences without compromising efficiency.
Original languageEnglish
Title of host publication2014 Brazilian Conference on Intelligent Systems (BRACIS)
PublisherIEEE Computer Society
ISBN (Print)978-1-4799-5618-0
Publication statusPublished - Oct 2014

Bibliographical note

(selected for special issue, blind reviewed by >2 reviewers)


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