Matthieu Petit

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.

Bayesian Annotation Networks for Complex Sequence Analysis

Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analysed. Their composition forms, in a natural way, a Bayesian network, and we show how standard methods for prediction and training can be adapted for such composite models in an iterative way, obtaining reasonable complexity results. Our methodology can be implemented using the probabilistic-logic PRISM system, developed by Sato et al, in a way that allows for practical applications.

An ongoing work on statistical structural testing via probabilistic concurrent constraint programming

The use of a model to describe and test the expected behavior of a program is a well-proved software testing technique. Statistical structural testing aims at building a model from which an input probability distribution can be derived that maximizes the coverage of some structural criteria by a random test data generator. Our approach consists in converting statistical structural testing into a probabilistic concurrent constraint programming (PCCP) problem in order 1) to exploit the high declarativity of the probabilistic choice operators of this paradigm and 2) to benefit from its automated constraint solving capacity. This paper reports on an ongoing work to implement PCCP and exploit it to solve instances of statistical structural testing problems. Application to testing Java Card applets is discussed.

A Declarative Pipeline Language for Complex Data Analysis

We introduce BANpipe – a logic-based scripting language designed to model complex compositions of time consuming analyses. Its declarative semantics is described together with alternative operational semantics facilitating goal directed execution, parallel execution, change propagation and type checking. A portable implementation is provided, which supports expressing complex pipelines that may integrate different Prolog systems and provide automatic management of files.