Particle MCMC algorithms and architectures for accelerating inference in state-space models

DOI10.1016/j.ijar.2016.10.011zbMath1404.68030OpenAlexW2551841786WikidataQ38857377 ScholiaQ38857377MaRDI QIDQ518646

Christos-Savvas Bouganis, Leonardo Bottolo, Grigorios Mingas

Publication date: 29 March 2017

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2016.10.011

zbMATH Keywords

Markov chain Monte CarloBayesian inferencefield programmable gate arrayparticle filterhardware acceleration

Mathematics Subject Classification ID

Bayesian inference (62F15) Monte Carlo methods (65C05) Performance evaluation, queueing, and scheduling in the context of computer systems (68M20) Mathematical problems of computer architecture (68M07)

Related Items (4)

Unconventional computing for Bayesian inference ⋮ Statistical modelling of cell movement ⋮ A novel T-S fuzzy particle filtering algorithm based on fuzzy C-regression clustering ⋮ Full Bayesian inference in hidden Markov models of plant growth

Uses Software

• LibBi
• WGBSSuite

Cites Work

• The pseudo-marginal approach for efficient Monte Carlo computations
• Particle filtering: the need for speed
• Parallel multivariate slice sampling
• On the efficiency of pseudo-marginal random walk Metropolis algorithms
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Handbook of Markov Chain Monte Carlo
• Particle Markov Chain Monte Carlo Methods
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
• On Information and Sufficiency
• Scalable inference for Markov processes with intractable likelihoods
• Unnamed Item
• Unnamed Item

This page was built for publication: Particle MCMC algorithms and architectures for accelerating inference in state-space models