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Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable
  Particle Filters Within Langevin Proposals

Enhanced SMC2^22: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

24 July 2024
Conor Rosato
Joshua Murphy
Alessandro Varsi
P. Horridge
Simon Maskell
ArXivPDFHTML

Papers citing "Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals"

3 / 3 papers shown
Title
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
Andrew Millard
Joshua Murphy
Simon Maskell
Zheng Zhao
BDL
32
0
0
01 May 2025
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Andrew Millard
Joshua Murphy
Daniel Frisch
Simon Maskell
BDL
41
0
0
03 Apr 2025
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
132
3,263
0
09 Jun 2012
1