ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1511.01707
  4. Cited By
Getting Started with Particle Metropolis-Hastings for Inference in
  Nonlinear Dynamical Models

Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

5 November 2015
J. Dahlin
Thomas B. Schon
ArXivPDFHTML

Papers citing "Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models"

3 / 3 papers shown
Title
Designing Proposal Distributions for Particle Filters using Integrated
  Nested Laplace Approximation
Designing Proposal Distributions for Particle Filters using Integrated Nested Laplace Approximation
A. Amri
13
1
0
05 May 2023
Efficient Learning of the Parameters of Non-Linear Models using
  Differentiable Resampling in Particle Filters
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Conor Rosato
Vincent Beraud
P. Horridge
Thomas B. Schon
Simon Maskell
8
14
0
02 Nov 2021
Sequential Monte Carlo Methods in the nimble R Package
Sequential Monte Carlo Methods in the nimble R Package
Nick Michaud
P. de Valpine
Daniel Turek
C. Paciorek
D. Nguyen
13
6
0
17 Mar 2017
1