Inference with constrained hidden Markov models in PRISM

Henning Christiansen, Christian Theil Have, Ole Torp Lassen, Matthieu Petit

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review


A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming has advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.
TidsskriftTheory and Practice of Logic Programming
Udgave nummer4-6
Sider (fra-til)449-464
Antal sider15
StatusUdgivet - 2010


  • inferens

Citer dette