Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments

DOI10.1108/S0731-905320140000034003zbMath1453.62615arXiv1304.4334OpenAlexW2952769901MaRDI QIDQ5133541

Garland B. Durham, John F. Geweke

Publication date: 10 November 2020

Published in: Advances in Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1304.4334

zbMATH Keywords

marginal likelihoodsequential Monte Carloparticle filterBayesian model comparisongraphics processing unitposterior simulation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian problems; characterization of Bayes procedures (62C10) Monte Carlo methods (65C05) Sequential estimation (62L12)

Related Items (17)

Bayesian CV@R/super-quantile regression ⋮ Striated Metropolis-Hastings sampler for high-dimensional models ⋮ A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design ⋮ Sequential Bayesian inference for vector autoregressions with stochastic volatility ⋮ Relevant parameter changes in structural break models ⋮ Global robust Bayesian analysis in large models ⋮ Finite sample complexity of sequential Monte Carlo estimators on multimodal target distributions ⋮ Hamiltonian sequential Monte Carlo with application to consumer choice behavior ⋮ Bayesian learning in performance. Is there any? ⋮ Finite-sample complexity of sequential Monte Carlo estimators ⋮ A non-linear Keynesian Goodwin-type endogenous model of the cycle: Bayesian evidence for the USA ⋮ Sequentially adaptive Bayesian learning algorithms for inference and optimization ⋮ Tempered particle filtering ⋮ Importance sampling from posterior distributions using copula-like approximations ⋮ Testing for persistence in US mutual funds' performance: a Bayesian dynamic panel model ⋮ On parallel implementation of sequential Monte Carlo methods: the island particle model ⋮ Bayesian Inference for ARFIMA Models

This page was built for publication: Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments