Testing and improving local adaptive importance sampling in LJF local-JT in multiply sectioned Bayesian networks

Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems. The exact inference is costly and difficult to be applied in the context of MSBNs. So the approximate inference is used as an alternative. Recently, for reasoning in MSBNs, LJF-based Local Adaptive Importance Sampler (LLAIS) has been developed for approximate reasoning in MSBNs. However, the prototype of LLAIS is tested on Alarm Network (37 nodes). But further testing on larger networks has not been reported. In this thesis, LLAIS algorithm is tested on three large networks namely Hailfinder (56 nodes), Win95pts (76 nodes) and PathFinder (109 nodes), to measure for its reliability and scalability. The experiments done show that LLAIS without parameters tuned shows good convergence for Hailfinder and Win95pts but not for Pathfinder network. However, when the parameters are tuned the algorithm shows considerable improvement in its accuracy for all the three networks tested

Efficient Probabilistic Inference Algorithms for Cooperative Multiagent Systems

Probabilistic reasoning methods, Bayesian networks (BNs) in particular, have emerged as an effective and central tool for reasoning under uncertainty. In a multi-agent environment, agents equipped with local knowledge often need to collaborate and reason about a larger uncertainty domain. Multiply sectioned Bayesian networks (MSBNs) provide a solution for the probabilistic reasoning of cooperative agents in such a setting. In this thesis, we first aim to improve the efficiency of current MSBN exact inference algorithms. We show that by exploiting the calculation schema and the semantic meaning of inter-agent messages, we can significantly reduce an agent\u27s local computational cost as well as the inter-agent communication overhead. Our novel technical contributions include 1) a new message passing architecture based on an MSBN linked junction tree forest (LJF); 2) a suite of algorithms extended from our work in BNs to provide the semantic analysis of inter-agent messages; 3) a fast marginal calibration algorithm, designed for an LJF that guarantees exact results with a minimum local and global cost. We then investigate how to incorporate approximation techniques in the MSBN framework. We present a novel local adaptive importance sampler (LLAIS) designed to apply localized stochastic sampling while maintaining the LJF structure. The LLAIS sampler provides accurate estimations for local posterior beliefs and promotes efficient calculation of inter-agent messages. We also address the problem of online monitoring for cooperative agents. As the MSBN model is restricted to static domains, we introduce an MA-DBN model based on a combination of the MSBN and dynamic Bayesian network (DBN) models. We show that effective multi-agent online monitoring with bounded error is possible in an MA-DBN through a new secondary inference structure and a factorized representation of forward messages

IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function

Exact computation of the partition function is known to be intractable, necessitating approximate inference techniques. Existing methods for approximate inference are slow to converge for many benchmarks. The control of accuracy-complexity trade-off is also non-trivial in many of these methods. We propose a novel incremental build-infer-approximate (IBIA) framework for approximate inference that addresses these issues. In this framework, the probabilistic graphical model is converted into a sequence of clique tree forests (SCTF) with bounded clique sizes. We show that the SCTF can be used to efficiently compute the partition function. We propose two new algorithms which are used to construct the SCTF and prove the correctness of both. The first is an algorithm for incremental construction of CTFs that is guaranteed to give a valid CTF with bounded clique sizes and the second is an approximation algorithm that takes a calibrated CTF as input and yields a valid and calibrated CTF with reduced clique sizes as the output. We have evaluated our method using several benchmark sets from recent UAI competitions and our results show good accuracies with competitive runtimes

Probabilistic modeling and reasoning in multiagent decision systems

Ph.DDOCTOR OF PHILOSOPH

Using causal knowledge to improve retrieval and adaptation in case-based reasoning systems for a dynamic industrial process

Case-based reasoning (CBR) is a reasoning paradigm that starts the reasoning process by examining past similar experiences. The motivation behind this thesis lies in the observation that causal knowledge can guide case-based reasoning in dealing with large and complex systems as it guides humans. In this thesis, case-bases used for reasoning about processes where each case consists of a temporal sequence are considered. In general, these temporal sequences include persistent and transitory (non-persistent) attributes. As these sequences tend to be long, it is unlikely to find a single case in the case-base that closely matches the problem case. By utilizing causal knowledge in the form of a dynamic Bayesian network (DBN) and exploiting the independence implied by the structure of the network and known attributes, this system matches independent portions of the problem case to corresponding sub-cases from the case-base. However, the matching of sub-cases has to take into account the persistence properties of attributes. The approach is then applied to a real life temporal process situation involving an automotive curing oven, in which a vehicle moves through stages within the oven to satisfy some thermodynamic relationships and requirements that change from stage to stage. In addition, testing has been conducted using data randomly generated from known causal networks. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .T54. Source: Masters Abstracts International, Volume: 45-01, page: 0366. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Bayesian Networks and Operational Risk

Texto dispoñible en galego e españolDescrición elemental do mecanismo de cálculo dunha rede bayesiana, destacando a importancia desta ferramenta na estimación do risco operacional en banca, dentro do marco das novas normas emanadas dos Acordos de Basilea para a xestión de riscos. O traballo inclúe dúas recompilacións de “software” sobre redes bayesianas, tanto gratuíto coma comercial, coas súas principais características, obtidas de internet, como unha axuda para iniciar un traballo en profundidade sobre o temaDescripción elemental del mecanismo de cálculo de una red bayesiana, destacando la importancia de esta herramienta en la estimación del riesgo operacional en banca, dentro del marco de las nuevas normas emanadas de los Acuerdos de Basilea para la gestión de riesgos. El trabajo incluye dos recopilaciones de “software” sobre redes bayesianas, tanto gratuito como comercial, con sus principales características, obtenidas de internet, como una ayuda para iniciar un trabajo en profundidad sobre el temaElemental description of the evaluation processs o a bayesian network, highlighting the importante of this tool in relation with the estimation of the operacional risk in banking. A new application of the Bayesian networks as consequence of the norms derived from Basel Accords, regulating risk management. The paper includes a pair of software reference sets, free and commercial, about Bayesian networks, with its main characteristics, obtained from internet. Is a help to initiate a more profound work about the matterS

Anatomical Image Series Analysis in the Computational Anatomy Random Orbit Model

Serially acquired medical imagery plays an important role in the computational study of human anatomy. In this work, we describe the development of novel algorithms set in the large deformation diffeomorphic metric mapping framework for analyzing serially acquired imagery of two general types: spatial image series and temporal image series. In the former case, a critical step in the analysis of neural connectivity from serially-sectioned brain histology data is the reconstruction of spatially distorted image volumes and registration into a common coordinate space. In the latter case, computational methods are required for building low dimensional representations of the infinite dimensional shape space standard to computational anatomy. Here, we review the vast body of work related to volume reconstruction and atlas-mapping of serially-sectioned data as well as diffeomorphic methods for longitudinal data and we position our work relative to these in the context of the computational anatomy random orbit model. We show how these two problems are embedded as extensions to the classic random orbit model and use it to both enforce diffeomorphic conditions and analyze the distance metric associated to diffeomorphisms. We apply our new algorithms to histology and MRI datasets to study the structure, connectivity, and pathological degeneration of the brain

Observations probabilistes dans les réseaux bayésiens

In a Bayesian network, evidence on a variable usually signifies that this variable is instantiated, meaning that the observer can affirm with certainty that the variable is in the signaled state. This thesis focuses on other types of evidence, often called uncertain evidence, which cannot be represented by the simple assignment of the variables. This thesis clarifies and studies different concepts of uncertain evidence in a Bayesian network and offers various applications of uncertain evidence in Bayesian networks.Firstly, we present a review of uncertain evidence in Bayesian networks in terms of terminology, definition, specification and propagation. It shows that the vocabulary is not clear and that some terms are used to represent different concepts.We identify three types of uncertain evidence in Bayesian networks and we propose the followingterminology: likelihood evidence, fixed probabilistic evidence and not-fixed probabilistic evidence. We define them and describe updating algorithms for the propagation of uncertain evidence. Finally, we propose several examples of the use of fixed probabilistic evidence in Bayesian networks. The first example concerns evidence on a subpopulation applied in the context of a geographical information system. The second example is an organization of agent encapsulated Bayesian networks that have to collaborate together to solve a problem. The third example concerns the transformation of evidence on continuous variables into fixed probabilistic evidence. The algorithm BN-IPFP-1 has been implemented and used on medical data from CHU Habib Bourguiba in Sfax.Dans un réseau bayésien, une observation sur une variable signifie en général que cette variable est instanciée. Ceci signifie que l’observateur peut affirmer avec certitude que la variable est dans l’état signalé. Cette thèse porte sur d’autres types d’observations, souvent appelées observations incertaines, qui ne peuvent pas être représentées par la simple affectation de la variable. Cette thèse clarifie et étudie les différents concepts d’observations incertaines et propose différentes applications des observations incertaines dans les réseaux bayésiens.Nous commençons par dresser un état des lieux sur les observations incertaines dans les réseaux bayésiens dans la littérature et dans les logiciels, en termes de terminologie, de définition, de spécification et de propagation. Il en ressort que le vocabulaire n'est pas clairement établi et que les définitions proposées couvrent parfois des notions différentes.Nous identifions trois types d’observations incertaines dans les réseaux bayésiens et nous proposons la terminologie suivante : observation de vraisemblance, observation probabiliste fixe et observation probabiliste non-fixe. Nous exposons ensuite la façon dont ces observations peuvent être traitées et propagées.Enfin, nous donnons plusieurs exemples d’utilisation des observations probabilistes fixes dans les réseaux bayésiens. Le premier exemple concerne la propagation d'observations sur une sous-population, appliquée aux systèmes d'information géographique. Le second exemple concerne une organisation de plusieurs agents équipés d'un réseau bayésien local et qui doivent collaborer pour résoudre un problème. Le troisième exemple concerne la prise en compte d'observations sur des variables continues dans un RB discret. Pour cela, l'algorithme BN-IPFP-1 a été implémenté et utilisé sur des données médicales de l'hôpital Bourguiba de Sfax

Graphical modeling of asymmetric games and value of information in multi-agent decision systems

Master'sMASTER OF ENGINEERIN