Design, Implementation, and Evaluation of the Constraint Language cc(FD)
Selected Papers from Constraint Programming: Basics and Trends
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
Non-discriminating Arguments and Their Uses
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Learning and inference with constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Generative modeling with failure in PRISM
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
New advances in logic-based probabilistic modeling by PRISM
Probabilistic inductive logic programming
CLP(BN): constraint logic programming for probabilistic knowledge
Probabilistic inductive logic programming
Belief bisimulation for hidden markov models: logical characterisation and decision algorithm
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
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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.