Results 21 to 30 of about 151,127 (170)
Sequentially Constrained Monte Carlo [PDF]
Computational Statistics & Data Analysis, 2016 Constraints can be interpreted in a broad sense as any kind of explicit restriction over the parameters. While some constraints are defined directly on the parameter space, when they are instead defined by known behaviour on the model, transformation of constraints into features on the parameter space may not be possible.
Shirin Golchi, David A. Campbell
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Statistical modeling for laser induced damage threshold
Computational Science and Techniques, 2021 Monte Carlo experiments are an efficient tool for investigation of the Laser-Induced Damage Threshold (LIDT) testing with pulsed lasers. In this study, the approach of sequential Monte Carlo search is developed for LIDT testing with bundle of laser ...
Leonidas Sakalauskas +1 more
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Elements of Sequential Monte Carlo [PDF]
Foundations and Trends® in Machine Learning, 2019 A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution.
Naesseth, Christian A. +2 more
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Kernel Sequential Monte Carlo [PDF]
, 2017 We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space.
Ingmar Schuster +3 more
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Monte Carlo Solutions for Blind Phase Noise Estimation
EURASIP Journal on Wireless Communications and Networking, 2009 This paper investigates the use of Monte Carlo sampling methods for phase noise estimation on additive white Gaussian noise (AWGN) channels. The main contributions of the paper are (i) the development of a Monte Carlo framework for phase noise estimation,
Frederik Simoens +4 more
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Sequential Monte Carlo Instant Radiosity [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2016 Instant Radiosity and its derivatives are interactive methods for efficiently estimating global (indirect) illumination. They represent the last indirect bounce of illumination before the camera as the composite radiance field emitted by a set of virtual point light sources (VPLs).
Hedman, Peter +3 more
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Sequential Monte Carlo without likelihoods [PDF]
Proceedings of the National Academy of Sciences, 2007 Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient and ...
Sisson, Scott, Fan, Yanan, Tanaka, Mark
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Waste-Free Sequential Monte Carlo [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2021 AbstractA standard way to move particles in a sequential Monte Carlo (SMC) sampler is to apply several steps of a Markov chain Monte Carlo (MCMC) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow.
Dau, Hai-Dang, Chopin, Nicolas
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Divide-and-Conquer With Sequential Monte Carlo [PDF]
Journal of Computational and Graphical Statistics, 2017 We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conquer approach based upon an auxiliary tree-structured decomposition of the model of interest, turning the overall inferential task into a collection of recursively solved sub ...
Lindsten, F +6 more
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Bayesian optimization with informative parametric models via sequential Monte Carlo
Data-Centric Engineering, 2022 Bayesian optimization (BO) has been a successful approach to optimize expensive functions whose prior knowledge can be specified by means of a probabilistic model.
Rafael Oliveira +7 more
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