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Operations for Learning with Graphical Models
 Journal of Artificial Intelligence Research
, 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
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Cited by 276 (13 self)
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are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas
Particle Filters for Positioning, Navigation and Tracking
, 2002
"... A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the part ..."
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Cited by 219 (23 self)
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A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious
G: Models for Discrete Longitudinal Data
 and Chen 6 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
"... This book covers a wide variety of statistical techniques for longitudinal data analysis. The authors, Geert Molenberghs and Geert Verbeke –both well known in this field – have extended their previous textbook (Verbeke and Molenberghs, 1997), mainly focused on linear mixed model for continuous data, ..."
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Cited by 172 (16 self)
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This book covers a wide variety of statistical techniques for longitudinal data analysis. The authors, Geert Molenberghs and Geert Verbeke –both well known in this field – have extended their previous textbook (Verbeke and Molenberghs, 1997), mainly focused on linear mixed model for continuous data
FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges
"... In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM that overcomes important deficiencies of the original algorithm. We prove convergence of this ..."
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Cited by 225 (7 self)
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of this new algorithm for linear SLAM problems and provide realworld experimental results that illustrate an order of magnitude improvement in accuracy over the original FastSLAM algorithm. 1
A tutorial on particle filtering and smoothing: fifteen years later
 OXFORD HANDBOOK OF NONLINEAR FILTERING
, 2011
"... Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. r ..."
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Cited by 214 (15 self)
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Optimal estimation problems for nonlinear nonGaussian statespace models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i
Particle Filters for State Estimation of Jump Markov Linear Systems
, 2001
"... Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulationbased algorithms called particle filter ..."
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Cited by 177 (15 self)
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filters to solve the optimal filtering problem as well as the optimal fixedlag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure
Temporal autocorrelation in univariate linear modelling of fMRI data
 pP Y C W P k nk N p Var(Yk ) (Yk ) 0 1 C CR 1 Var(Y ) P k nk N Var(Y k ) 0 1 C MI H(X;Y ) H(X) H(Y ) 1 0 C NMI H(X;Y ) H(X)+H(Y
, 2000
"... In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or “color ..."
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Cited by 211 (10 self)
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In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
, 2000
"... The problem of similarity search in large time series databases has attracted much attention recently. It is a nontrivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing th ..."
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Cited by 240 (21 self)
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The problem of similarity search in large time series databases has attracted much attention recently. It is a nontrivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing
An Adaptive ColorBased Particle Filter
, 2002
"... Robust realtime tracking of nonrigid objects is a challenging task. Particle filtering has proven very successful for nonlinear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination wi ..."
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Cited by 160 (5 self)
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Robust realtime tracking of nonrigid objects is a challenging task. Particle filtering has proven very successful for nonlinear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination
SPIRAL: Code Generation for DSP Transforms
 PROCEEDINGS OF THE IEEE SPECIAL ISSUE ON PROGRAM GENERATION, OPTIMIZATION, AND ADAPTATION
"... Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL that considers this problem for the performancecritical domain of linear digital signal proces ..."
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Cited by 222 (41 self)
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Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL that considers this problem for the performancecritical domain of linear digital signal
Results 21  30
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4,537