Inference with Constrained Hidden Markov Models in PRISM

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 have 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

Constrained hidden Markov models for population-based haplotyping

Peer reviewe

Introduction to the 26th International Conference on Logic Programming Special Issue

This is the preface to the 26th International Conference on Logic Programming Special IssueComment: 6 page

Introduction to technical communications of the 26th Int'l. Conference on logic programming (ICLP'10)

Abstract is not available

Introduction to the Technical Communications of the 26th International Conference on Logic Programming

Introduction to the Technical Communications of the 26th International Conference on Logic Programming

Robust partial-learning in linear Gaussian systems

International audienceThis paper deals with unsupervised and off-line learning of parameters involved in linear Gaussian systems, i.e. the estimation of the transition and the noise covariances matrices of a state-space system from a finite series of observations only. In practice, these systems are the result of a physical problem for which there is a partial knowledge either on the sensors from which the observations are issued or on the state of the studied system. We therefore propose in this work an " Expectation-Maximization " learning type algorithm that takes into account constraints on parameters such as the fact that two identical sensors have the same noise characteristics, and so estimation procedure should exploit this knowledge. The algorithms are designed for the pairwise linear Gaussian system that takes into account supplementary cross-dependences between observations and hidden states w.r.t. the conventional linear system, while still allowing optimal filtering by means of a Kalman-like filter. The algorithm is made robust through QR decompositions and the propagation of a square-root of the covariance matrices instead of the matrices themselves. It is assessed through a series of experiments that compare the algorithms which incorporate or not partial knowledge, for short as well as for long signals

Using Population Mixtures to Optimize the Utility of Genomic Databases: Linkage Disequilibrium and Association Study Design in India

When performing association studies in populations that have not been the focus of large-scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis – such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65949/1/j.1469-1809.2008.00457.x.pd