Probabilistic Independence Networks for Hidden Markov Probability Models

Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach

Three Modern Roles for Logic in AI

We consider three modern roles for logic in artificial intelligence, which are based on the theory of tractable Boolean circuits: (1) logic as a basis for computation, (2) logic for learning from a combination of data and knowledge, and (3) logic for reasoning about the behavior of machine learning systems.Comment: To be published in PODS 202

User-driven Page Layout Analysis of historical printed Books

International audienceIn this paper, based on the study of the specificity of historical printed books, we first explain the main error sources in classical methods used for page layout analysis. We show that each method (bottom-up and top-down) provides different types of useful information that should not be ignored, if we want to obtain both a generic method and good segmentation results. Next, we propose to use a hybrid segmentation algorithm that builds two maps: a shape map that focuses on connected components and a background map, which provides information about white areas corresponding to block separations in the page. Using this first segmentation, a classification of the extracted blocks can be achieved according to scenarios produced by the user. These scenarios are defined very simply during an interactive stage. The user is able to make processing sequences adapted to the different kinds of images he is likely to meet and according to the user needs. The proposed “user-driven approach” is capable of doing segmentation and labelling of the required user high level concepts efficiently and has achieved above 93% accurate results over different data sets tested. User feedbacks and experimental results demonstrate the effectiveness and usability of our framework mainly because the extraction rules can be defined without difficulty and parameters are not sensitive to page layout variation

A probabilistic reasoning and learning system based on Bayesian belief networks

SIGLEAvailable from British Library Document Supply Centre- DSC:DX173015 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Generalized belief change with imprecise probabilities and graphical models

We provide a theoretical investigation of probabilistic belief revision in complex frameworks, under extended conditions of uncertainty, inconsistency and imprecision. We motivate our kinematical approach by specializing our discussion to probabilistic reasoning with graphical models, whose modular representation allows for efficient inference. Most results in this direction are derived from the relevant work of Chan and Darwiche (2005), that first proved the inter-reducibility of virtual and probabilistic evidence. Such forms of information, deeply distinct in their meaning, are extended to the conditional and imprecise frameworks, allowing further generalizations, e.g. to experts' qualitative assessments. Belief aggregation and iterated revision of a rational agent's belief are also explored

Lifted graphical models: a survey

Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field

Issues in the Bayesian forecasting of dispersal after a nuclear accident

This thesis addresses three main topics related to the practical problems of modelling the spread of nuclear material after an accidental release. The first topic deals with the issue of how qualitative information (expert jUdgement) about the development of the emission of contamination after an accident can be coded as a Dynamic Linear Model (DLM). An illustration is given of the subsequent adaptation of the expert judgement in response to the incoming data. Moreover, the height of the release at the source can be a key parameter in the subsequent dispersal. We addressed uncertainty on the release height using the Multi-Process Models framework. That is we included several models in our analysis, each with a different release height. The Bayesian methodology uses probabilities representing their relative likelihood to weight these and updates the probabilities in the light of monitoring data. A brief illustration of testing the updating algorithm on simulated contamination readings is provided. The second topic concerns the demands of computational efficiency. We show how the Bayesian propagation algorithms on a dynamic junction tree of cliques of variables (representing a high dimensional Gaussian process), as provided by Smith et al. (1995), can be generalised to incorporate the case when data may destroy neat dependencies (i.e. when observations are taken under more than one clique). Here we introduce two classes of new operators: exact and non-exact (approximations) which act on this high dimensional Gaussian process, modifying its junction tree by another tree which allows quicker probability propagation. We also develop fast algorithms which can be defined by approximating Gaussian systems by cutting edges on junctions. The appropriateness ofthe approximations is based on the Kulback-Leibler/Hellinger distances. Some of these new operators and algorithms have been implemented and coded. Preliminary tests on these algorithms were carried out using arbitrary data, and the system proved to be highly efficient in terms of P.C. user time. The third topic concentrates on generalisations from a Gaussian process. It proposes, as a good approximation, an adaptation of the Dynamic Generalised Linear Models (DGLMs) of West, Harrison, and Migon (1985) for updating algorithms on a dynamic junction tree. The Hellinger distance is used to check the accuracy of the dynamic approximation. The analysis of these topics involves a review and extension of some useful theory and results on Bayesian forecasting and dynamic models, graphical modelling, and information divergence