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Optimized Auxiliary Particle Filters: adapting mixture proposals via
  convex optimization
v1v2 (latest)

Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization

Conference on Uncertainty in Artificial Intelligence (UAI), 2020
18 November 2020
Nicola Branchini
Victor Elvira
ArXiv (abs)PDFHTML

Papers citing "Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization"

11 / 11 papers shown
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networksSignal Processing (Signal Process.), 2024
Benjamin Cox
Santiago Segarra
Victor Elvira
475
1
0
23 Nov 2024
Normalizing Flow-based Differentiable Particle Filters
Normalizing Flow-based Differentiable Particle Filters
Xiongjie Chen
Yunpeng Li
264
0
0
03 Mar 2024
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space
  Models
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space ModelsIEEE Transactions on Signal Processing (IEEE TSP), 2023
Benjamin Cox
Victor Elvira
261
13
0
20 Jun 2023
Designing Proposal Distributions for Particle Filters using Integrated
  Nested Laplace Approximation
Designing Proposal Distributions for Particle Filters using Integrated Nested Laplace Approximation
A. Amri
268
1
0
05 May 2023
Properties of Marginal Sequential Monte Carlo Methods
Properties of Marginal Sequential Monte Carlo MethodsSocial Science Research Network (SSRN), 2023
F. R. Crucinio
A. M. Johansen
211
4
0
06 Mar 2023
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Wenhan Li
Xiongjie Chen
Wenwu Wang
Victor Elvira
Yunpeng Li
386
9
0
20 Feb 2023
An overview of differentiable particle filters for data-adaptive
  sequential Bayesian inference
An overview of differentiable particle filters for data-adaptive sequential Bayesian inferenceFoundations of Data Science (FDS), 2023
Xiongjie Chen
Yunpeng Li
383
34
0
19 Feb 2023
Unsupervised Learning of Sampling Distributions for Particle Filters
Unsupervised Learning of Sampling Distributions for Particle FiltersIEEE Transactions on Signal Processing (IEEE TSP), 2023
Fernando Gama
Nicolas Zilberstein
Martín Sevilla
Richard Baraniuk
Santiago Segarra
254
12
0
02 Feb 2023
The Lifebelt Particle Filter for robust estimation from low-valued count
  data
The Lifebelt Particle Filter for robust estimation from low-valued count dataFoundations of Data Science (FODS), 2022
Alice Corbella
T. McKinley
Paul J. Birrell
A. Presanis
S. Spencer
Gareth O. Roberts
Daniela De Angelis
227
3
0
08 Dec 2022
Graphical Inference in Linear-Gaussian State-Space Models
Graphical Inference in Linear-Gaussian State-Space ModelsIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022
Victor Elvira
Émilie Chouzenoux
185
21
0
20 Sep 2022
Variational Marginal Particle Filters
Variational Marginal Particle Filters
Jinlin Lai
Justin Domke
Daniel Sheldon
315
14
0
30 Sep 2021
1
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