Algorithms for hidden Markov models with imprecisely specified parameters

Denis Deratani Mauá, Cassio P. de Campos, Alessandro Antonucci

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)


Hidden Markov models (HMMs) are widely used models for sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, which can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs 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. In this paper, we formalize iHMMs and develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations. Experiments with real data show that iHMMs produce more reliable inferences without compromising efficiency.

Original languageEnglish
Title of host publicationBRACIS 2014 :2014 Brazilian Conference on Intelligent Systems : proceedings : 19-23 October 2014, São Carlos, São Paulo, Brazil
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-4799-5618-0
Publication statusPublished - 2014
Externally publishedYes
Event3rd Brazilian Conference on Intelligent Systems (BRACIS 2014) - Sao Carlos, Sao Paulo, Brazil
Duration: 19 Oct 201423 Oct 2014
Conference number: 3


Conference3rd Brazilian Conference on Intelligent Systems (BRACIS 2014)
Abbreviated titleBRACIS 2014
CitySao Carlos, Sao Paulo


  • Hidden markov models
  • Imprecise probability
  • Probabilistic graphical models
  • Sensitivity analysis


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