Results 31 to 40 of about 151,127 (170)
vSMC: Parallel Sequential Monte Carlo in C++
Journal of Statistical Software, 2015 Sequential Monte Carlo is a family of algorithms for sampling from a sequence of distributions. Some of these algorithms, such as particle filters, are widely used in physics and signal processing research. More recent developments have established their
Yan Zhou
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On Sequential Bayesian Inference for Continual Learning
Entropy, 2023 Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
Samuel Kessler +4 more
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A sequential Monte Carlo approach to computing tail probabilities in stochastic models [PDF]
, 2011 Sequential Monte Carlo methods which involve sequential importance sampling and resampling are shown to provide a versatile approach to computing probabilities of rare events.
Chan, Hock Peng, Lai, Tze Leung
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Sequential Monte Carlo: A Unified Review
Annual Review of Control, Robotics, and Autonomous Systems, 2023 Sequential Monte Carlo methods—also known as particle filters—offer approximate solutions to filtering problems for nonlinear state-space systems. These filtering problems are notoriously difficult to solve in general due to a lack of closed-form expressions and challenging expectation integrals.
Wills, Adrian G., Schön, Thomas B.
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Online Variational Sequential Monte Carlo
CoRR, 2023 Being the most classical generative model for serial data, state-space models (SSM) are fundamental in AI and statistical machine learning. In SSM, any form of parameter learning or latent state inference typically involves the computation of complex latent-state posteriors.
Alessandro Mastrototaro, Jimmy Olsson
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SoftwareX, 2018 With proliferation of smart devices such as smart phones, it is common that an event is recorded by multiple individuals creating several audio and video perspectives. Such user generated content is mostly unorganized (not synchronized). In this work, we
Dogac Basaran +2 more
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Sequential Monte Carlo multiple testing. [PDF]
Bioinformatics, 2011 AbstractMotivation: In molecular biology, as in many other scientific fields, the scale of analyses is ever increasing. Often, complex Monte Carlo simulation is required, sometimes within a large-scale multiple testing setting. The resulting computational costs may be prohibitively high.Results: We here present MCFDR, a simple, novel algorithm for ...
Sandve GK, Ferkingstad E, Nygård S.
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Sequential Monte Carlo Bandits
CoRR, 2013 In this paper we propose a flexible and efficient framework for handling multi-armed bandits, combining sequential Monte Carlo algorithms with hierarchical Bayesian modeling techniques. The framework naturally encompasses restless bandits, contextual bandits, and other bandit variants under a single inferential model. Despite the model's generality, we
Michael Cherkassky, Luke Bornn
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Evolutionary Sequential Monte Carlo Samplers for Change-Point Models
Econometrics, 2016 Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain ...
Arnaud Dufays
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INVESTIGATION OF BIOLOGICAL OBJECTS IN OPTICAL COHERENCE TOMOGRAPHY WITH DATA PROCESSING BY SEQUENTIAL MONTE CARLO METHOD [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2014 A possibility of sequential Monte Carlo method application for data processing in the full-field optical coherent tomography for studying of biological objects is demonstrated.
M. A. Volynsky +2 more
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