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49
Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
 Psychological Review
, 2006
"... A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probab ..."
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Cited by 36 (7 self)
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A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trialbytrial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The backpropagation of target values to attention allows the model to show trialorder effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.
A generalpurpose tunable landscape generator
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2006
"... The research literature on metaheuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging realworld optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are ma ..."
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Cited by 14 (3 self)
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The research literature on metaheuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging realworld optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metaheuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the largescale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (testproblem) generator that can be used to generate optimization problem instances for continuous, boundconstrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.
Causal Inference and Reasoning in Causally Insufficient Systems
, 2006
"... The big question that motivates this dissertation is the following: under what conditions and to what extent can passive observations inform us of the structure of causal connections among a set of variables and of the potential outcome of an active intervention on some of the variables? The partic ..."
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Cited by 11 (2 self)
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The big question that motivates this dissertation is the following: under what conditions and to what extent can passive observations inform us of the structure of causal connections among a set of variables and of the potential outcome of an active intervention on some of the variables? The particular concern here revolves around the common kind of situations where the variables of interest, though measurable themselves, may suffer from confounding due to unobserved common causes. Relying on a graphical representation of causally insufficient systems called maximal ancestral graphs, and two wellknown principles widely discussed in the literature, the causal Markov and Faithfulness conditions, we show that the FCI algorithm, a sound inference procedure in the literature for inferring features of the unknown causal structure from facts of probabilistic independence and dependence, is, with some extra sound inference rules, also complete in the sense that any feature of the causal structure left undecided by the inference procedure is indeed underdetermined by facts of probabilistic independence and dependence. In addition, we consider the issue of quantitative reasoning about effects of local interventions with the FCIlearnable features of the unknown causal structure. We improve and generalize two important pieces of work in the literature about identifying intervention effects. We also provide some preliminary study of the testability of the
Analyzing air combat simulation results with dynamic Bayesian networks
 in: Proceedings of the 2007 Winter Simulation Conference
, 2007
"... permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Aalto University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purpose ..."
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Cited by 8 (4 self)
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permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Aalto University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to
Online Feature Selection with Streaming Features
, 2012
"... We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is ..."
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Cited by 8 (4 self)
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We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for online streaming feature selection include (1) the continuous growth of feature volumes over time; (2) a large feature space, possibly of unknown or infinite size; and (3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection (OSFS) method to select strongly relevant and nonredundant features on the fly. An efficient FastOSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on highdimensional datasets and also with a realworld case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.
Models for prediction, explanation and control: recursive Bayesian networks. Theoria
, 2011
"... The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilit ..."
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Cited by 7 (2 self)
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The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple twolevel RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell’s
0406). A Probabilistic Multidimensional Data Model and Algebra for OLAP in Decision Support Systems. Paper presented at the
 Proceedings of the IEEE SoutheastCon'03, Ocho Rios
, 2003
"... A probabilistic multidimensional data model and its applications in business management ..."
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Cited by 6 (0 self)
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A probabilistic multidimensional data model and its applications in business management
WeC15.3 Bayesian Networks for Cardiovascular Monitoring
"... Abstract — Bayesian Networks provide a flexible way of incorporating different types of information into a single probabilistic model. In a medical setting, one can use these networks to create a patient model that incorporates lab test results, clinician observations, vital signs, and other forms o ..."
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Cited by 3 (3 self)
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Abstract — Bayesian Networks provide a flexible way of incorporating different types of information into a single probabilistic model. In a medical setting, one can use these networks to create a patient model that incorporates lab test results, clinician observations, vital signs, and other forms of patient data. In this paper, we explore a simple Bayesian Network model of the cardiovascular system and evaluate its ability to predict unobservable variables using both real and simulated patient data. I.
Uncorrelated Encounter Model of the National Airspace System Version 1.0
, 2008
"... Approved for public release; distribution is unlimited. Lexington Airspace encounter models, covering close encounter situations that may occur after standard separation assurance has been lost, are a critical component in the safety assessment of aviation procedures and collision avoidance systems. ..."
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Cited by 3 (0 self)
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Approved for public release; distribution is unlimited. Lexington Airspace encounter models, covering close encounter situations that may occur after standard separation assurance has been lost, are a critical component in the safety assessment of aviation procedures and collision avoidance systems. Of particular relevance to Unmanned Aircraft Systems (UAS) is the potential for encountering general aviation aircraft that are flying under Visual Flight Rules (VFR) and which may not be in contact with air traffic control. In response to the need to develop a model of these types of encounters, Lincoln Laboratory undertook an extensive radar data collection and modeling effort involving more than 120 sensors across the U.S. This report describes the structure and content of that encounter model. The model is based on the use of Bayesian networks to represent relationships between dynamic variables and to construct random aircraft trajectories that are statistically similar to those observed in the radar data. The result is a framework
Risk Analysis
 in Project Management, Project Management
, 1987
"... A formal method for cost and accuracy tradeoff ..."
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