Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models

DOI10.1080/10618600.2021.1923519OpenAlexW3159497698WikidataQ114099333 ScholiaQ114099333MaRDI QIDQ5066477

Tore Selland Kleppe, Roman Liesenfeld, Kjartan Kloster Osmundsen

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2021.1923519

zbMATH Keywords

latent variablesstate-space modelMCMCdynamic rescalingstan

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates ⋮ Efficient data augmentation techniques for some classes of state space models

Uses Software

• R
• R-INLA
• Rcpp
• BayesDA
• LibBi

Cites Work

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• Efficient high-dimensional importance sampling
• Testing the assumptions behind importance sampling
• On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
• The conditional autoregressive Wishart model for multivariate stock market volatility
• Stochastic volatility in asset prices. Estimation with simulated maximum likelihood
• Markov chain Monte Carlo with the integrated nested Laplace approximation
• Efficient importance sampling in mixture frameworks
• Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets
• Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS
• Simulating Hamiltonian Dynamics
• Transport Map Accelerated Markov Chain Monte Carlo
• Practical Markov Chain Monte Carlo
• Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models
• Accurate Approximations for Posterior Moments and Marginal Densities
• Likelihood analysis of non-Gaussian measurement time series
• On the application of improved symplectic integrators in Hamiltonian Monte Carlo
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Particle Markov Chain Monte Carlo Methods
• Fast Reverse-Mode Automatic Differentiation using Expression Templates in C++
• Handbook of Volatility Models and Their Applications
• The Gibbs sampler with particle efficient importance sampling for state-space models*