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Bayesian Modality Fusion: Probabilistic Integration Of Multiple Vision Algorithms for Head Tracking
 FOURTH ASIAN CONFERENCE ON COMPUTER VISION (ACCV)
, 2000
"... We describe a headtracking system that harnesses Bayesian modality fusion, a technique for integrating the analyses of multiple visual tracking algorithms within a probabilistic framework. At the heart of the approach is a Bayesian network model that includes random variables that serve as context ..."
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Cited by 48 (3 self)
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We describe a headtracking system that harnesses Bayesian modality fusion, a technique for integrating the analyses of multiple visual tracking algorithms within a probabilistic framework. At the heart of the approach is a Bayesian network model that includes random variables that serve as contextsensitive indicators of reliability of the different tracking algorithms. Parameters of the Bayesian model are learned from data in an offline training phase using groundtruth data from a Polhemus tracking device. In our implementation for a realtime head tracking task, algorithms centering on color, motion, and background subtraction modalities are fused into a single estimate of head position in an image. Results demonstrate the effectiveness of Bayesian modality fusion in environments undergoing a variety of visual perturbances.
Learning Bayesian Networks: A unification for discrete and Gaussian domains
 PROCEEDINGS OF ELEVENTH CONFERENCE ON UNCERTAINTY INARTI CIAL INTELLIGENCE
, 1995
"... We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both do ..."
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Cited by 45 (7 self)
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We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with wellknown statistical facts about the Dirichlet and normal{Wishart distributions to derive our metrics for discrete and Gaussian domains.
Active Learning of Causal Bayes Net Structure
, 2001
"... We propose a decision theoretic approach for deciding which interventions to perform so as to learn the causal structure of a model as quickly as possible. Without such interventions, it is impossible to distinguish between Markov equivalent models, even given infinite data. We perform online MCMC t ..."
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Cited by 37 (2 self)
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We propose a decision theoretic approach for deciding which interventions to perform so as to learn the causal structure of a model as quickly as possible. Without such interventions, it is impossible to distinguish between Markov equivalent models, even given infinite data. We perform online MCMC to estimate the posterior over graph structures, and use importance sampling to find the best action to perform at each step. We assume the data is discretevalued and fully observed.
Userexpertise modeling with empirically derived probabilistic implication networks
, 1996
"... ..."
Theorybased causal inference
 In
, 2003
"... People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data – often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top ..."
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Cited by 27 (2 self)
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People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data – often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong topdown prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottomup models of inference. 1
Likelihoods and Parameter Priors for Bayesian Networks
, 1995
"... We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that permit the construction of likelihoods and parameter priors for a large number of Bayesiannetwork s ..."
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Cited by 23 (0 self)
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We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that permit the construction of likelihoods and parameter priors for a large number of Bayesiannetwork structures from a small set of assessments. The most notable assumption is that of likelihood equivalence, which says that data can not help to discriminate network structures that encode the same assertions of conditional independence. We describe the constructions that follow from these assumptions, and also present a method for directly computing the marginal likelihood of a random sample with no missing observations. Also, we show how these assumptions lead to a general framework for characterizing parameter priors of multivariate distributions. Keywords: Bayesian network, learning, likelihood equivalence, Dirichlet, normalWishart. 1 Introduction A Bayesian network is a graphical repres...
Learning Causal Networks from Data: A survey and a new algorithm for recovering possibilistic causal networks
, 1997
"... Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For example, diagnosis is usually defined as the task of finding the causes (illnesses) from the observed effects (symptoms). Similarly, prediction can be understood as the description of a future plausible ..."
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Cited by 19 (5 self)
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Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For example, diagnosis is usually defined as the task of finding the causes (illnesses) from the observed effects (symptoms). Similarly, prediction can be understood as the description of a future plausible situation where observed effects will be in accordance with the known causal structure of the phenomenon being studied. Causal models are a summary of the knowledge about a phenomenon expressed in terms of causation. Many areas of the ap # This work has been partially supported by the Spanish Comission Interministerial de Ciencia y Tecnologia Project CICYTTIC96 0878. plied sciences (econometry, biomedics, engineering, etc.) have used such a term to refer to models that yield explanations, allow for prediction and facilitate planning and decision making. Causal reasoning can be viewed as inference guided by a causation theory. That kind of inference can be further specialised into induc
The markov assumption in spoken dialogue management
 In 6th SIGDial Workshop on Discourse and Dialogue
, 2005
"... The goal of dialogue management in a spoken dialogue system is to take actions based on observations and inferred beliefs. To ensure that the actions optimize the performance or robustness of the system, researchers have turned to reinforcement learning methods to learn policies for action selection ..."
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Cited by 13 (0 self)
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The goal of dialogue management in a spoken dialogue system is to take actions based on observations and inferred beliefs. To ensure that the actions optimize the performance or robustness of the system, researchers have turned to reinforcement learning methods to learn policies for action selection. To derive an optimal policy from data, the dynamics of the system is often represented as a Markov Decision Process (MDP), which assumes that the state of the dialogue depends only on the previous state and action. In this paper, we investigate whether constraining the state space by the Markov assumption, especially when the structure of the state space may be unknown, truly affords the highest reward. In a simulation experiment conducted in the context of a dialogue system for interacting with a speechenabled web browser, models under the Markov assumption did not perform as well as an alternative model which attempts to classify the total reward with accumulating features. We discuss the implications of the study as well as limitations. 1
Knowing and reasoning in
 in College: Gender Related Patterns in Student’s Intellectual Development
, 1992
"... Modelling a decision support system for ..."
2006) Computational inference of neural information flow networks PLoS Computational Biology 2:e161
, 2006
"... Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are ..."
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Cited by 9 (3 self)
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Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higherorder auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to nonneural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.