Langevin incremental mixture importance sampling

DOI10.1007/S11222-017-9747-5zbMath1384.65012arXiv1611.06874OpenAlexW2552349744MaRDI QIDQ1703855

Flávio Eler de Melo, Matteo Fasiolo, Simon Maskell

Publication date: 7 March 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1611.06874

zbMATH Keywords

importance samplingmixture densityKalman-Bucy filterlocal approximationLangevin diffusionoptimal importance distribution

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (5)

Daisee: Adaptive importance sampling by balancing exploration and exploitation ⋮ Gradient-based adaptive importance samplers ⋮ Efficient Bayes inference in neural networks through adaptive importance sampling ⋮ Variance analysis of multiple importance sampling schemes ⋮ LIMIS

Uses Software

• UCI-ml
• Stan
• Julia
• BRENT
• GitHub
• NUTS

Cites Work

• Unnamed Item
• Unnamed Item
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• NUTS
• Exponential convergence of Langevin distributions and their discrete approximations
• Optimal scaling for various Metropolis-Hastings algorithms.
• Julia: A Fresh Approach to Numerical Computing
• Estimating and Projecting Trends in HIV/AIDS Generalized Epidemics Using Incremental Mixture Importance Sampling
• Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data
• Local Adaptive Importance Sampling for Multivariate Densities With Strong Nonlinear Relationships
• Sequential Imputations and Bayesian Missing Data Problems
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• An Introduction to Numerical Analysis
• An adaptive Metropolis algorithm

This page was built for publication: Langevin incremental mixture importance sampling