Abstract
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 language | English |
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Title of host publication | BRACIS 2014 :2014 Brazilian Conference on Intelligent Systems : proceedings : 19-23 October 2014, São Carlos, São Paulo, Brazil |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 186-191 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-4799-5618-0 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 3rd Brazilian Conference on Intelligent Systems (BRACIS 2014) - Sao Carlos, Sao Paulo, Brazil Duration: 19 Oct 2014 → 23 Oct 2014 Conference number: 3 |
Conference
Conference | 3rd Brazilian Conference on Intelligent Systems (BRACIS 2014) |
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Abbreviated title | BRACIS 2014 |
Country/Territory | Brazil |
City | Sao Carlos, Sao Paulo |
Period | 19/10/14 → 23/10/14 |
Keywords
- Hidden markov models
- Imprecise probability
- Probabilistic graphical models
- Sensitivity analysis