Results 1  10
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2,389
Iterative decoding of binary block and convolutional codes
 IEEE TRANS. INFORM. THEORY
, 1996
"... Iterative decoding of twodimensional systematic convolutional codes has been termed “turbo” (de)coding. Using loglikelihood algebra, we show that any decoder can he used which accepts soft inputsincluding a priori valuesand delivers soft outputs that can he split into three terms: the soft chann ..."
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Cited by 610 (43 self)
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Iterative decoding of twodimensional systematic convolutional codes has been termed “turbo” (de)coding. Using loglikelihood algebra, we show that any decoder can he used which accepts soft inputsincluding a priori valuesand delivers soft outputs that can he split into three terms: the soft
Consensus in the presence of partial synchrony
 JOURNAL OF THE ACM
, 1988
"... The concept of partial synchrony in a distributed system is introduced. Partial synchrony lies between the cases of a synchronous system and an asynchronous system. In a synchronous system, there is a known fixed upper bound A on the time required for a message to be sent from one processor to ano ..."
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Cited by 513 (18 self)
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to another and a known fixed upper bound (I, on the relative speeds of different processors. In an asynchronous system no fixed upper bounds A and (I, exist. In one version of partial synchrony, fixed bounds A and (I, exist, but they are not known a priori. The problem is to design protocols that work
Measuring phasesynchrony in brain signals
 Hum. Brain Mapp
, 1999
"... r r Abstract: This article presents, for the first time, a practical method for the direct quantification of frequencyspecific synchronization (i.e., transient phaselocking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronie ..."
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Cited by 346 (6 self)
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coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also
Analysis of fMRI Data by Blind Separation Into Independent Spatial Components
 HUMAN BRAIN MAPPING 6:160–188(1998)
, 1998
"... Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent comp ..."
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Cited by 317 (18 self)
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Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent
Parallel Preconditioning with Sparse Approximate Inverses
 SIAM J. Sci. Comput
, 1996
"... A parallel preconditioner is presented for the solution of general sparse linear systems of equations. A sparse approximate inverse is computed explicitly, and then applied as a preconditioner to an iterative method. The computation of the preconditioner is inherently parallel, and its application o ..."
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Cited by 226 (10 self)
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only requires a matrixvector product. The sparsity pattern of the approximate inverse is not imposed a priori but captured automatically. This keeps the amount of work and the number of nonzero entries in the preconditioner to a minimum. Rigorous bounds on the clustering of the eigenvalues
LAPLACE PRIORI VERSUS JEFFREYS PRIORI: WHICH ONE?
"... ABSTRACT: A process to select a noninformative prior distribution makes the user to be faced to several possibilities and he/she thinks that only under some theoretical conditions one could be considered better than other, but in the applied problem this becomes irrelevant. We provide three basic e ..."
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examples that show that the choice is important and it depends on the underlying sample distributions, but also, on parameter values.
Bayesian inference on phylogeny and its impact on evolutionary biology.
 Science
, 2001
"... 1 As a discipline, phylogenetics is becoming transformed by a flood of molecular data. These data allow broad questions to be asked about the history of life, but also present difficult statistical and computational problems. Bayesian inference of phylogeny brings a new perspective to a number of o ..."
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Cited by 235 (10 self)
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). Usually all trees are considered a priori equally probable, and the likelihood is calculated under one of a number of standard Markov models of character evolution. The posterior probability, although easy to formulate, involves a summation over all trees and, for each tree, integration over all possible
A Combinatorial Algorithm Minimizing Submodular Functions in Strongly Polynomial Time
, 1999
"... We give a strongly polynomialtime algorithm minimizing a submodular function f given by a valuegiving oracle. The algorithm does not use the ellipsoid method or any other linear programming method. No bound on the complexity of the values of f is needed to be known a priori. The number of oracle ..."
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Cited by 201 (0 self)
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We give a strongly polynomialtime algorithm minimizing a submodular function f given by a valuegiving oracle. The algorithm does not use the ellipsoid method or any other linear programming method. No bound on the complexity of the values of f is needed to be known a priori. The number of oracle
GALERKIN FINITE ELEMENT APPROXIMATIONS OF STOCHASTIC ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS
, 2004
"... We describe and analyze two numerical methods for a linear elliptic problem with stochastic coefficients and homogeneous Dirichlet boundary conditions. Here the aim of the computations is to approximate statistical moments of the solution, and, in particular, we give a priori error estimates for the ..."
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Cited by 193 (11 self)
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We describe and analyze two numerical methods for a linear elliptic problem with stochastic coefficients and homogeneous Dirichlet boundary conditions. Here the aim of the computations is to approximate statistical moments of the solution, and, in particular, we give a priori error estimates
Reinforcement Learning In Continuous Time and Space
 Neural Computation
, 2000
"... This paper presents a reinforcement learning framework for continuoustime dynamical systems without a priori discretization of time, state, and action. Based on the HamiltonJacobiBellman (HJB) equation for infinitehorizon, discounted reward problems, we derive algorithms for estimating value f ..."
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Cited by 176 (7 self)
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This paper presents a reinforcement learning framework for continuoustime dynamical systems without a priori discretization of time, state, and action. Based on the HamiltonJacobiBellman (HJB) equation for infinitehorizon, discounted reward problems, we derive algorithms for estimating value
Results 1  10
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2,389