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74
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linearGaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of
applying RaoBlackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization
and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.
Modelling gene expression data using dynamic bayesian networks
, 1999
"... Recently, there has been much interest in reverse engineering genetic networks from time series data. In this paper, we show that most of the proposed discrete time models — including the boolean network model [Kau93, SS96], the linear model of D’haeseleer et al. [DWFS99], and the nonlinear model of ..."
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Cited by 223 (1 self)
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Recently, there has been much interest in reverse engineering genetic networks from time series data. In this paper, we show that most of the proposed discrete time models — including the boolean network model [Kau93, SS96], the linear model of D’haeseleer et al. [DWFS99], and the nonlinear model of Weaver et al. [WWS99] — are all special cases of a general class of models called Dynamic Bayesian Networks (DBNs). The advantages of DBNs include the ability to model stochasticity, to incorporate prior knowledge, and to handle hidden variables and missing data in a principled way. This paper provides a review of techniques for learning DBNs. Keywords: Genetic networks, boolean networks, Bayesian networks, neural networks, reverse engineering, machine learning. 1
Probabilistic Propositional Planning: Representations and Complexity
 In Proceedings of the Fourteenth National Conference on Artificial Intelligence
, 1997
"... Many representations for probabilistic propositional planning problems have been studied. This paper reviews several such representations and shows that, in spite of superficial differences between the representations, they are "expressively equivalent," meaning that planning problems ..."
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Cited by 87 (11 self)
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Many representations for probabilistic propositional planning problems have been studied. This paper reviews several such representations and shows that, in spite of superficial differences between the representations, they are "expressively equivalent," meaning that planning problems specified in one representation can be converted to equivalent planning problems in any of the other representations with at most a polynomial increase in the resulting representation and the number of steps needed to reach the goal with sufficient probability. The paper proves that the computational complexity of determining whether a successful plan exists for planning problems expressed in any of these representations is EXPTIMEcomplete and PSPACEcomplete when plans are restricted to take a polynomial number of steps. Introduction In recent years, there has been an interest in solving planning problems that contain some degree of uncertainty. One form that this uncertainty has taken ...
Stratified exponential families: Graphical models and model selection
 ANNALS OF STATISTICS
, 2001
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MiniBuckets: A General Scheme for Approximating Inference
 Journal of ACM
, 1998
"... The paper presents a class of approximation algorithms that extend the idea of bounded inference, inspired by successful constraint propagation algorithms, to probabilistic inference and combinatorial optimization. The idea is to bound the dimensionality of dependencies created by inference algor ..."
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Cited by 40 (18 self)
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The paper presents a class of approximation algorithms that extend the idea of bounded inference, inspired by successful constraint propagation algorithms, to probabilistic inference and combinatorial optimization. The idea is to bound the dimensionality of dependencies created by inference algorithms. This yields a parameterized scheme, called minibuckets, that offers adjustable levels of accuracy and efficiency. The minibucket approach generates both an approximate solution and a bound on the solution quality. We present empirical results demonstrating successful performance of the proposed approximation scheme for probabilistic tasks, both on randomly generated problems and on realistic domains such as medical diagnosis and probabilistic decoding. 1 Introduction Automated reasoning tasks such as constraint satisfaction and optimization, probabilistic inference, decisionmaking, and planning are generally hard (NPhard). One way to cope This work was partially supported...
Learning firstorder probabilistic models with combining rules
 IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE IN MACHINE LEARNING
, 2005
"... Many realworld domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models describe probabilistic influences between attributes of objects that are related to each other through known domain relatio ..."
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Cited by 38 (15 self)
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Many realworld domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models describe probabilistic influences between attributes of objects that are related to each other through known domain relationships. To keep these models succinct, each such influence is considered independent of others, which is called the assumption of “independence of causal influences” (ICI). In this paper, we describe a language that consists of quantified conditional influence statements and captures most relational probabilistic models based on directed graphs. The influences due to different statements are combined using a set of combining rules such as NoisyOR. We motivate and introduce multilevel combining rules, where the lower level rules combine the influences due to different ground instances of the same statement, and the upper level rules combine the influences due to different statements. We present algorithms and empirical results for parameter learning in the presence of such combining rules. Specifically, we derive and implement algorithms based on gradient descent and expectation maximization for different combining rules and evaluate them on synthetic data and on a realworld task. The results demonstrate that the algorithms are able to learn both the conditional probability distributions of the influence statements and the parameters of the combining rules.
Adaptive Bayesian logic programs
 PROCEEDINGS OF THE ELEVENTH CONFERENCE ON INDUCTIVE LOGIC PROGRAMMING (ILP01), VOLUME 2157 OF LNCS
, 2001
"... First order probabilistic logics combine a first order logic with a probabilistic knowledge representation. In this context, we introduce continuous Bayesian logic programs, which extend the recently introduced Bayesian logic programs to deal with continuous random variables. Bayesian logic programs ..."
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Cited by 30 (10 self)
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First order probabilistic logics combine a first order logic with a probabilistic knowledge representation. In this context, we introduce continuous Bayesian logic programs, which extend the recently introduced Bayesian logic programs to deal with continuous random variables. Bayesian logic programs tightly integrate definite logic programs with Bayesian networks. The resulting framework nicely seperates the qualitative (i.e. logical) component from the quantitative (i.e. the probabilistic) one. We also show how the quantitative component can be learned using a gradientbased maximum likelihood method.
Word sense disambiguation based on semantic density
 In Proceedings of the ColingACL'98 Workshop “Usage of WordNet in Natural Language Processing Systems
, 1998
"... This paper presents a Word Sense Disambiguation method based on the idea of semantic density between words. The disambiguation is done in the context of WordNet. The Internet is used as a raw corpora to provide statistical information for word associations. A metric is introduced and used to measure ..."
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Cited by 27 (3 self)
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This paper presents a Word Sense Disambiguation method based on the idea of semantic density between words. The disambiguation is done in the context of WordNet. The Internet is used as a raw corpora to provide statistical information for word associations. A metric is introduced and used to measure the semantic density and to rank all possible combinations of the senses of two words. This method provides a precision of 58 % in indicating the correct sense for both words at the same time. The precision increases as we consider more choices: 70 % for top two ranked and 7'3 % for top three ranked. 1
DecisionTheoretic Troubleshooting: A Framework for Repair and Experiment
 IN PROCEEDINGS OF THE TWELFTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1996
"... We develop and extend existing decisiontheoretic methods for troubleshooting a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has focused on belief updating  determining the probabilities of various faults given current observations. In this paper, we extend this paradi ..."
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Cited by 24 (0 self)
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We develop and extend existing decisiontheoretic methods for troubleshooting a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has focused on belief updating  determining the probabilities of various faults given current observations. In this paper, we extend this paradigm to include taking actions. In particular, we consider three classes of actions: (1) we can make observations regarding the behavior of a device and infer likely faults as in traditional diagnosis, (2) we can repair a component and then observe the behavior of the device to infer likely faults, and (3) we can change the configuration of the device, observe its new behavior, and infer the likelihood of faults. Analysis of latter two classes of troubleshooting actions requires incorporating notions of persistence into the beliefnetwork formalism used for probabilistic inference.
Basic Principles of Learning Bayesian Logic Programs
 Institute for Computer Science, University of Freiburg
, 2002
"... Bayesian logic programs tightly integrate definite logic programs with Bayesian networks in order to... In this paper, we present results on combining Inductive Logic Programming with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs from data ..."
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Cited by 24 (3 self)
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Bayesian logic programs tightly integrate definite logic programs with Bayesian networks in order to... In this paper, we present results on combining Inductive Logic Programming with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs from data. More precisely, we show how the qualitative components can be learned by combining the inductive logic programming setting learning from interpretations with scorebased techniques for learning Bayesian networks. The estimation of the quantitative components is reduced to the corresponding problem of (dynamic) Bayesian networks