Results 31 to 40 of about 2,663,721 (268)
Steerable Importance Sampling [PDF]
2007 IEEE Symposium on Interactive Ray Tracing, 2007 We present an algorithm for efficient stratified importance sampling of environment maps that generates samples in the positive hemisphere defined by local orientation of arbitrary surfaces while accounting for cosine weighting. The importance function is dynamically adjusted according to the surface normal using steerable basis functions.
Kartic Subr, James Arvo
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Statistics and Computing, 2001 Simulated annealing - moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions - has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an ...
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Unconstrained recursive importance sampling
The Annals of Applied Probability, 2010 We propose an unconstrained stochastic approximation method of finding the optimal measure change (in an a priori parametric family) for Monte Carlo simulations. We consider different parametric families based on the Girsanov theorem and the Esscher transform (or exponential-tilting).
Lemaire, Vincent, Pagès, Gilles
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A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities
Frontiers in Neuroscience, 2018 Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of ...
Wenqiong Xue +2 more
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EXACT SIMULATION OF A BOOLEAN MODEL
Image Analysis and Stereology, 2013 A Boolean model is a union of independent objects (compact random subsets) located at Poisson points. Two algorithms are proposed for simulating a Boolean model in a bounded domain. The first one applies only to stationary models.
Christian Lantuéjoul
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Research on Data-Driven Optimal Scheduling of Power System
Energies, 2023 The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid.
Jianxun Luo +4 more
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Support Vector Machine-Assisted Importance Sampling for Optimal Reliability Design
Applied Sciences, 2022 A population-based optimization algorithm combining the support vector machine (SVM) and importance sampling (IS) is proposed to achieve a global solution to optimal reliability design.
Chunyan Ling, Jingzhe Lei, Way Kuo
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Rare event simulation via importance sampling for linear SPDE's [PDF]
, 2017 The goal of this paper is to develop provably efficient importance sampling Monte Carlo methods for the estimation of rare events within the class of linear stochastic partial differential equations (SPDEs).
Salins, Michael +1 more
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Advantage estimator based on importance sampling
Tongxin xuebao, 2019 In continuous action tasks,deep reinforcement learning usually uses Gaussian distribution as a policy function.Aiming at the problem that the Gaussian distribution policy function slows down due to the clipped action,an importance sampling advantage ...
Quan LIU, Yubin JIANG, Zhihui HU
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Applied Sciences, 2019 This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed.
Jilin Cai +3 more
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