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.
- Hidden Markov Model with side-constraints
- Programming in statistical modeling
Christiansen, H., Have, C. T., Lassen, O. T., & Petit, M. (2010). Inference with constrained hidden Markov models in PRISM. Theory and Practice of Logic Programming, 10(4-6), 449-464. https://doi.org/10.1017/S1471068410000219