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234,676
Weighted Importance Sampling Techniques for Monte Carlo Radiosity
, 2000
"... This paper presents weighted importance sampling techniques for Monte Carlo form factor computation and for stochastic Jacobi radiosity system solution. Weighted importance sampling is a generalisation of importance sampling. The basic idea is to compute aposteriori a correction factor to the i ..."
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Cited by 6 (0 self)
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This paper presents weighted importance sampling techniques for Monte Carlo form factor computation and for stochastic Jacobi radiosity system solution. Weighted importance sampling is a generalisation of importance sampling. The basic idea is to compute aposteriori a correction factor
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
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Cited by 1032 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples
Sequential Monte Carlo Methods for Dynamic Systems
 Journal of the American Statistical Association
, 1998
"... A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ..."
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Cited by 650 (12 self)
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A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
 Biometrika
, 1995
"... Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determi ..."
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Cited by 1330 (24 self)
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Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model
Bandit based MonteCarlo Planning
 In: ECML06. Number 4212 in LNCS
, 2006
"... Abstract. For large statespace Markovian Decision Problems MonteCarlo planning is one of the few viable approaches to find nearoptimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide MonteCarlo planning. In finitehorizon or discounted MDPs the algo ..."
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Cited by 433 (7 self)
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Abstract. For large statespace Markovian Decision Problems MonteCarlo planning is one of the few viable approaches to find nearoptimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide MonteCarlo planning. In finitehorizon or discounted MDPs
QuasiMonte Carlo Radiosity
, 1996
"... The problem of global illumination in computer graphics is described by a second kind Fredholm integral equation. Due to the complexity of this equation, Monte Carlo methods provide an interesting tool for approximating solutions to this transport equation. For the case of the radiosity equation, w ..."
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Cited by 40 (2 self)
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The problem of global illumination in computer graphics is described by a second kind Fredholm integral equation. Due to the complexity of this equation, Monte Carlo methods provide an interesting tool for approximating solutions to this transport equation. For the case of the radiosity equation
A rapid hierarchical radiosity algorithm
 Computer Graphics
, 1991
"... This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing lar e polygonal patches. The afgorithm constructs a hierarchic“J representation of the form factor matrix by adaptively subdividing patches into su bpatches according to a usersupplied error bound. The a ..."
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Cited by 412 (11 self)
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. The algorithm guarantees that all form factors are calculated to the same precision, removing many common image artifacts due to inaccurate form factors. More importantly, the al orithm decomposes the form factor matrix into at most O? n) blocks (where n is the number of elements). Previous radiosity
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Incorporating nonlocal information into information extraction systems by gibbs sampling
 In ACL
, 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
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Cited by 696 (25 self)
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, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate nonlocal structure while preserving tractable inference. We
Results 1  10
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234,676