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2,547
and probabilistic interpretation of
, 1996
"... Configurational transition in a FlemingViottype model ..."
Probabilistic Interpretation of Population Codes
, 1998
"... We present a general encodingdecoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the p ..."
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Cited by 111 (16 self)
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not only about a single value of some quantity but also about its whole distribution, including its variance, and perhaps even the certainty the system has in the actual presence in the world of the entity generating this quantity. We propose a novel method for forming such probabilistic interpretations
A probabilistic interpretation of canonical correlation analysis
, 2005
"... We give a probabilistic interpretation of canonical correlation (CCA) analysis as a latent variable model for two Gaussian random vectors. Our interpretation is similar to the probabilistic interpretation of principal component analysis (Tipping and Bishop, 1999, Roweis, 1998). In addition, we can i ..."
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Cited by 104 (1 self)
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We give a probabilistic interpretation of canonical correlation (CCA) analysis as a latent variable model for two Gaussian random vectors. Our interpretation is similar to the probabilistic interpretation of principal component analysis (Tipping and Bishop, 1999, Roweis, 1998). In addition, we can
Probabilistic interpretation of mechanical motion
, 2013
"... Probabilistic approach to the description of translational motion of macrobodies indicates the emergence of additional order effects oriented in the direction of motion of the body Introduction. Today, the applicability of probabilistic description of particles motion in the microsystems (nucleus, ..."
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Probabilistic approach to the description of translational motion of macrobodies indicates the emergence of additional order effects oriented in the direction of motion of the body Introduction. Today, the applicability of probabilistic description of particles motion in the microsystems (nucleus
A Probabilistic Interpretation of the θMethod
, 2002
"... This paper gives a probabilistic interpretation of a class of finite difference schemes often referred to as the θmethod. In particular, the present paper shows that for some parameter values the θmethod can been seen as a binomial tree with a random time. ..."
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This paper gives a probabilistic interpretation of a class of finite difference schemes often referred to as the θmethod. In particular, the present paper shows that for some parameter values the θmethod can been seen as a binomial tree with a random time.
2015 Probabilistic interpretation of linear solvers
 SIAM J. Optim
"... Abstract. This manuscript proposes a probabilistic framework for algorithms that iteratively solve unconstrained linear problems Bx = b with positive definite B for x. The goal is to retain, at any time, instead of a point estimate, a Gaussian posterior belief over the elements of the inverse of B. ..."
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Cited by 2 (0 self)
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. Extending recent probabilistic interpretations of the secant family of quasiNewton numerical optimization algorithms, and combining them with properties of the conjugate gradient algorithm, leads to uncertaintycalibrated methods that have very limited cost overhead over conjugate gradients, a self
A Probabilistic Interpretation of the Saliency Network
 In ECCV00
, 2000
"... The calculation of salient structures is one of the early and basic ideas of perceptual organization in Computer Vision. Saliency algorithms aim to nd image curves, maximizing some deterministic quality measure which grows with the length of the curve, its smoothness, and its continuity. This n ..."
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Cited by 6 (1 self)
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. This note proposes a modi ed saliency estimation mechanism, which is based on probabilistically speci ed grouping cues and on length estimation. In the context of the proposed method, the wellknown saliency mechanism, proposed by Shaashua and Ullman [SU88], may be interpreted as a process trying
A PROBABILISTIC INTERPRETATION OF THE MACDONALD POLYNOMIALS
, 2012
"... The twoparameter Macdonald polynomials are a central object of algebraic combinatorics and representation theory. We give a Markov chain on partitions of k with eigenfunctions the coefficients of the Macdonald polynomials when expanded in the power sum polynomials. The Markov chain has stationary d ..."
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Cited by 5 (0 self)
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The twoparameter Macdonald polynomials are a central object of algebraic combinatorics and representation theory. We give a Markov chain on partitions of k with eigenfunctions the coefficients of the Macdonald polynomials when expanded in the power sum polynomials. The Markov chain has stationary distribution a new twoparameter family of measures on partitions, the inverse of the Macdonald weight (rescaled). The uniform distribution on cycles of permutations and the Ewens sampling formula are special cases. The Markov chain is a version of the auxiliary variables algorithm of statistical physics. Properties of the Macdonald polynomials allow a sharp analysis of the running time. In natural cases, a bounded number of steps suffice for arbitrarily large k.
Probabilistic Interpretations of Integrability for Game Dynamics∗
, 2013
"... In models of evolution and learning in games, a variety of proofs of convergence rely on the assumption that the players ’ choice functions are integrable. This assumption does not have an obvious gametheoretic interpretation. We address this question by introducing probability models defined in t ..."
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Cited by 1 (1 self)
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In models of evolution and learning in games, a variety of proofs of convergence rely on the assumption that the players ’ choice functions are integrable. This assumption does not have an obvious gametheoretic interpretation. We address this question by introducing probability models defined
The Probabilistic Interpretation of ModelBased Diagnosis
"... Abstract. Modelbased diagnosis is the field of research concerned with the problem of finding faults in systems by reasoning with abstract models of the systems. Typically, such models offer a description of the structure of the system in terms of a collection of interacting components. For each of ..."
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Cited by 1 (0 self)
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to incorporate uncertainty into the diagnostic reasoning process about the structure and behaviour of systems, since much of what goes on in a system cannot be observed. This paper proposes a method for decomposing the probability distribution underlying probabilistic modelbased diagnosis in two parts: (i
Results 1  10
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