Results 261 to 270 of about 61,960 (301)

Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation. [PDF]

PLoS Comput Biol
Temfack D, Wyse J.
europepmc   +1 more source

Point cloud deformation modeling for particle selection following cryo-EM 2D classification. [PDF]

BMC Bioinformatics
Wang X, Mo Z, Li F, Zhang F, Wan X.
europepmc   +1 more source

A Novel Denoising Method for Mud Continuous-Wave Signals Based on Selective Ensemble Strategy with Particle Swarm Optimization. [PDF]

Sensors (Basel)
Huang C, Cai W, Pang D, Yang Y, Du X, Duan X, Zhou Y.   +6 more
europepmc   +1 more source

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation. [PDF]

Structure
He L, Bartesaghi A.
europepmc   +1 more source

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Particle Filtering

IEEE Signal Processing Magazine, 2003
Petar M Djuric, Mónica F Bugallo, Joaquin Miguez   +2 more
exaly   +2 more sources

Copula Particle Filters

Computational Statistics & Data Analysis, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carlos E. Rodríguez, Stephen G. Walker
openaire   +1 more source

Parallel Particle Filtering

Journal of Parallel and Distributed Computing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Olivier Brun
exaly   +3 more sources

Particle Filters Revisited

2020 Australian and New Zealand Control Conference (ANZCC), 2020
Particle filters are often explained by either heuristics arguments or complex mathematics. Present day particle filters rely on various methods such as importance sampling, resampling method and resampling strategy. Moreover, there are different derivations for discrete and continuous time dynamic models. In this paper we offer a new simple derivation
Torben Knudsen, John Leth
openaire   +1 more source

Filtering via Simulation: Auxiliary Particle Filters [PDF]

Journal of the American Statistical Association, 1999
This article analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution. Here we tackle the first of these problems.
Michael K Pitt, Neil Shephard
exaly   +8 more sources