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38
Parameter estimation for text analysis
, 2004
"... Abstract. Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. ..."
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Abstract. Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. As an application, the model of latent Dirichlet allocation (LDA) is explained in detail with a full derivation of an approximate inference algorithm based on Gibbs sampling, including a discussion of Dirichlet hyperparameter estimation. Finally, analysis methods of LDA models are discussed.
A survey of probabilistic models, using the bayesian programming methodology as a unifying framework
 In The Second International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2003
, 2003
"... This paper presents a survey of the most common probabilistic models for artefact conception. We use a generic formalism called Bayesian Programming, which we introduce briefly, for reviewing the main probabilistic models found in the literature. Indeed, we show that Bayesian Networks, Markov Locali ..."
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This paper presents a survey of the most common probabilistic models for artefact conception. We use a generic formalism called Bayesian Programming, which we introduce briefly, for reviewing the main probabilistic models found in the literature. Indeed, we show that Bayesian Networks, Markov Localization, Kalman filters, etc., can all be captured under this single formalism. We believe it offers the novice reader a good introduction to these models, while still providing the experienced reader an enriching global view of the field. 1
Modeling Neuronal Interactivity using Dynamic Bayesian Networks
"... Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, usi ..."
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Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models for brain activation patterns. This framework is applied to modeling of neuronal circuits associated with reward. The novelty of our framework from a Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity models which are derived from fMRI data are then validated through a group classification task. We employ and compare four different types
Multimodal face tracking using Bayesian network
 In Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures
, 2003
"... This paper presents a Bayesian network based multimodal fusion method for robust and realtime face tracking. The Bayesian network integrates a prior of second order system dynamics, and the likelihood cues from color, edge and face appearance. While different modalities have different confidence sc ..."
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This paper presents a Bayesian network based multimodal fusion method for robust and realtime face tracking. The Bayesian network integrates a prior of second order system dynamics, and the likelihood cues from color, edge and face appearance. While different modalities have different confidence scales, we encode the environmental factors related to the confidences of modalities into the Bayesian network, and develop a Fisher discriminant analysis method for learning optimal fusion. The face tracker may track multiple faces under different poses. It is made up of two stages. First hypotheses are efficiently generated using a coarsetofine strategy; then multiple modalities are integrated in the Bayesian network to evaluate the posterior of each hypothesis. The hypothesis that maximizes a posterior (MAP) is selected as the estimate of the object state. Experimental results demonstrate the robustness and realtime performance of our face tracking approach. 1.
There’s an app for that, but it doesn’t work. Diagnosing Mobile Applications in the Wild
"... Abstract — There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we in ..."
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Abstract — There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we interviewed developers of five mobile applications and analyzed hundreds of trouble tickets. We find that support for diagnosing unexpected application behavior is lacking across major mobile platforms. Even when developers implement heavyweight logging during controlled trials, they do not discover many dependencies that are then stressed in the wild. They are also not wellequipped to understand how to monitor the large number of dependencies without impacting the phone’s limited resources such as CPU and battery. Based on these findings, we argue for three fundamental changes to failure reporting on mobile phones. The first is spatial spreading, which exploits the large number of phones in the field by spreading the monitoring work across them. The second is statistical inference, which builds a conditional distribution model between application behavior and its dependencies in the presence of partial information. The third is adaptive sampling, which dynamically varies what each phone monitors, to adapt to both the varying population of phones and what is being learned about each failure. We propose a system called MobiBug that combines these three techniques to simplify the task of diagnosing mobile applications. Categories and Subject Descriptors C.4 [Performance of systems] Reliability, availability, and serviceability D.2.5 [Testing and debugging] Distributed debugging C.2.4 [Distributed systems] Distributed applications
Probabilistic Consistency Analysis for Gene Selection
 in CSB. 2004
"... A great deal of recent research has focused on the problem of selecting differentially expressed genes from microarray data (‘gene selection’). Recent theoretical work has shown that the effectiveness of a gene selection algorithm can be captured as a probability called ‘selection accuracy’. Unfortu ..."
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A great deal of recent research has focused on the problem of selecting differentially expressed genes from microarray data (‘gene selection’). Recent theoretical work has shown that the effectiveness of a gene selection algorithm can be captured as a probability called ‘selection accuracy’. Unfortunately, in practice, there tends to be relatively little known about the very features upon which selection accuracy depends, making it difficult to choose a suitable method. In this paper we present a ‘consistency analysis’ which allows the inference of posterior distributions over selection accuracy from data. The utility of our approach lies in the fact that it can be used to assess gene selection algorithms in a practical but principled manner, and thus choose an appropriate method for given experimental data. 1.
BayesianNetworksBased Misuse and Anomaly Prevention System
 Proceedings of the Tenth International Conference on Enterprise Information Systems
"... Abstract: Network Intrusion Detection Systems (NIDS) aim at preventing network attacks and unauthorised remote use of computers. More accurately, depending on the kind of attack it targets, an NIDS can be oriented to detect misuses (by defining all possible attacks) or anomalies (by modelling legiti ..."
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Abstract: Network Intrusion Detection Systems (NIDS) aim at preventing network attacks and unauthorised remote use of computers. More accurately, depending on the kind of attack it targets, an NIDS can be oriented to detect misuses (by defining all possible attacks) or anomalies (by modelling legitimate behaviour and detecting those that do not fit on that model). Still, since their problem knowledge is restricted to possible attacks, misuse detection fails to notice anomalies and vice versa. Against this, we present here ESIDEDepian, the first unified misuse and anomaly prevention system based on Bayesian Networks to analyse completely network packets, and the strategy to create a consistent knowledge model that integrates misuse and anomalybased knowledge. Finally, we evaluate ESIDEDepian against wellknown and new attacks showing how it outperforms a wellestablished industrial NIDS. 1
Declarative Programming for Agent Applications
"... This paper introduces the computational model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higherorder functions, modal computation, probabilistic computation, and some theoremproving capa ..."
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This paper introduces the computational model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higherorder functions, modal computation, probabilistic computation, and some theoremproving capabilities. The need for these features is motivated and examples are given to illustrate the central ideas.
Troya, “Analyzing directed acyclic graph recombination
 in Computational Intelligence: Theory and Applications
"... Abstract. This work studies the edgebased representation of directed acyclic graphs, as well as the properties of recombination operators working on it. It is shown that this representation is not separable, and the structure of the basic information units that must be processed in order to maintai ..."
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Abstract. This work studies the edgebased representation of directed acyclic graphs, as well as the properties of recombination operators working on it. It is shown that this representation is not separable, and the structure of the basic information units that must be processed in order to maintain feasibility of the solutions is described. As an experimental analysis indicates, a recombination operator using these units has subquadratic complexity in the graph size. It is also shown that a standard genetransmission recombination operator is biased to produce solutions of lower edgedensity than the parents ’ average. An unbiased allelic recombination operator provides better results on an adhoc test problem. 1