Generalized parallel tempering on Bayesian inverse problems

DOI10.1007/s11222-021-10042-6zbMath1476.62015arXiv2003.03341OpenAlexW3010526759MaRDI QIDQ2058888

Juan P. Madrigal-Cianci, Raúl Tempone, Fabio Nobile, Jonas Latz

Publication date: 10 December 2021

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

Full work available at URL: https://arxiv.org/abs/2003.03341

zbMATH Keywords

Markov chain Monte Carloparallel temperinguncertainty quantificationBayesian inversioninfinite swapping

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Parallel numerical computation (65Y05)

Related Items (4)

Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem ⋮ Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings ⋮ Projective Integral Updates for High-Dimensional Variational Inference ⋮ Analysis of stochastic gradient descent in continuous time

Cites Work

• Unnamed Item
• State-dependent swap strategies and automatic reduction of number of temperatures in adaptive parallel tempering algorithm
• The pseudo-marginal approach for efficient Monte Carlo computations
• On the convergence of adaptive sequential Monte Carlo methods
• FEM-based discretization-invariant MCMC methods for PDE-constrained Bayesian inverse problems
• Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
• Markov chains and stochastic stability
• Automated solution of differential equations by the finite element method. The FEniCS book
• Multilevel sequential Monte Carlo for Bayesian inverse problems
• Numerical models for differential problems. Translated by Silvia Quarteroni.
• Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
• Rates of convergence for everywhere-positive Markov chains
• Geometric MCMC for infinite-dimensional inverse problems
• Markov chain decomposition for convergence rate analysis
• Dimension-independent likelihood-informed MCMC
• Stochastic simulation: Algorithms and analysis
• Explicit error bounds for Markov chain Monte Carlo
• Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A Case Study for the Navier--Stokes Equations
• A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow
• Handbook of Markov Chain Monte Carlo
• On the Infinite Swapping Limit for Parallel Tempering
• On the Well-posedness of Bayesian Inverse Problems
• hIPPYlib
• WASSERSTEIN METRIC-DRIVEN BAYESIAN INVERSION WITH APPLICATIONS TO SIGNAL PROCESSING
• Equation of State Calculations by Fast Computing Machines
• Bayesian Parameter Identification in Cahn--Hilliard Models for Biological Growth
• Analysis of the Ensemble Kalman Filter for Inverse Problems
• Monte Carlo sampling methods using Markov chains and their applications
• An adaptive Metropolis algorithm
• Sequential Monte Carlo methods for Bayesian elliptic inverse problems
• MCMC methods for functions: modifying old algorithms to make them faster

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