Multistream dynamic Bayesian network for meeting segmentation

Consonant duration is influenced by a number of linguistic factors such as the consonant s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the consonant s duration. Interactions between factors are represented as conditional dependency arcs in this graphical model. Given the parameters of the belief network, the duration of each consonant in the test set is then predicted as the value with the maximum probability. We compare the results of the belief network model with those of sums-of-products (SoP) and classification and regression tree (CART) models using the same data. In terms of RMS error, our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. In addition, the Bayesian model reliably predicts consonant duration in cases of missing or hidden linguistic factors

Experiences with Modelling Issues in Building Probabilistic Networks

Abstract. Building a probabilistic network for a real-life application is a difficult and time-consuming task. Methodologies for building such a network, however, are still lacking. Also, literature on network-specific modelling issues is quite scarce. As we have developed a large proba-bilistic network for a complex medical domain, we have encountered and resolved numerous non-trivial modelling issues. Since many of these is-sues pertain not only to our application but are likely to emerge for other applications as well, we feel that sharing them will contribute to engineering probabilistic networks in general.

Partially ordered models

We provide a formal definition and study the basic properties of partially ordered chains (POC). These systems were proposed to model textures in image processing and to represent independence relations between random variables in statistics (in the later case they are known as Bayesian networks). Our chains are a generalization of probabilistic cellular automata (PCA) and their theory has features intermediate between that of discrete-time processes and the theory of statistical mechanical lattice fields. Its proper definition is based on the notion of partially ordered specification (POS), in close analogy to the theory of Gibbs measure. This paper contains two types of results. First, we present the basic elements of the general theory of POCs: basic geometrical issues, definition in terms of conditional probability kernels, extremal decomposition, extremality and triviality, reconstruction starting from single-site kernels, relations between POM and Gibbs fields. Second, we prove three uniqueness criteria that correspond to the criteria known as bounded uniformity, Dobrushin and disagreement percolation in the theory of Gibbs measures.Comment: 54 pages, 11 figures, 6 simulations. Submited to Journal of Stat. Phy

A New Multi-Layers Method to Analyze Gene Expression

In the paper a new Multi-Layers approach called Multi-Layers Model MLM) for the analysis of stochastic signals and its application to the analysis of gene expression data is presented. It consists in the generation of sub-samples from the input signal by applying a threshold technique based on cut-set optimal conditions. The MLM has been applied on synthetic and real microarray data for the identification of particular regions across DNA called nucleosomes and linkers. Nucleosomes are the fundamental repeating subunits of all eukaryotic chromatin, and their positioning provides useful information regarding the regulation of gene expression in eukaryotic cells. Results have shown a good recognition rate on synthetic data, moreover, the MLM shows a good agreement with a recently published method based on Hidden Markov Model when tested on the Saccharomyces cerevisiae chromosomes microarray data

Designing a Procedure for the Acquisition of Probability Constraints for Bayesian Networks

Abstract. Among the various tasks involved in building a Bayesian network for a real-life application, the task of eliciting all probabilities required is generally considered the most daunting. We propose to sim-plify this task by first acquiring qualitative features of the probability distribution to be represented; these features can subsequently be taken as constraints on the precise probabilities to be obtained. We discuss the design of a procedure that guides the knowledge engineer in acquiring these qualitative features in an efficient way, based on an in-depth analy-sis of all viable combinations of features. In addition, we report on initial experiences with our procedure in the domain of neonatology.