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Optimized Auxiliary Particle Filters: adapting mixture proposals via
  convex optimization

Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization

18 November 2020
Nicola Branchini
Victor Elvira
ArXivPDFHTML

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

5 / 5 papers shown
Title
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 networks
Benjamin Cox
Santiago Segarra
Victor Elvira
78
0
0
23 Nov 2024
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space
  Models
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models
Benjamin Cox
Victor Elvira
41
10
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
29
1
0
05 May 2023
Properties of Marginal Sequential Monte Carlo Methods
Properties of Marginal Sequential Monte Carlo Methods
F. R. Crucinio
A. M. Johansen
26
2
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
43
6
0
20 Feb 2023
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