Stability of noisy Metropolis-Hastings
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Publication:341137
DOI10.1007/s11222-015-9604-3zbMath1505.62286arXiv1503.07066OpenAlexW2148980794WikidataQ59474067 ScholiaQ59474067MaRDI QIDQ341137
Publication date: 16 November 2016
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1503.07066
Markov chain Monte Carlogeometric ergodicityintractable likelihoodsMonte Carlo within Metropolispseudo-marginal Monte Carlo
Computational methods in Markov chains (60J22) Computational methods for problems pertaining to statistics (62-08) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05)
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Cites Work
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- The pseudo-marginal approach for efficient Monte Carlo computations
- Establishing some order amongst exact approximations of MCMCs
- Comparison of asymptotic variances of inhomogeneous Markov chains with application to Markov chain Monte Carlo methods
- On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
- General state space Markov chains and MCMC algorithms
- Computable bounds for geometric convergence rates of Markov chains
- Geometric ergodicity and hybrid Markov chains
- Simulation-based Bayesian inference for epidemic models
- Perturbation theory for Markov chains via Wasserstein distance
- On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
- Rates of convergence of the Hastings and Metropolis algorithms
- On the efficiency of pseudo-marginal random walk Metropolis algorithms
- Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
- Batch means and spectral variance estimators in Markov chain Monte Carlo
- On exponential ergodicity and spectral structure for birth-death processes. II
- Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
- Yet Another Look at Harris’ Ergodic Theorem for Markov Chains
- Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
- Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
- Markov Chains and Stochastic Stability
- Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
- Convergence Properties of Perturbed Markov Chains
- A Perturbation Theory for Ergodic Markov Chains and Application to Numerical Approximations
- Analyses of Infectious Disease Data from Household Outbreaks by Markov Chain Monte Carlo Methods
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Regular Perturbation of V-Geometrically Ergodic Markov Chains
- The Existence of the First Negative Moment Revisited
- Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
- Estimating Macroeconomic Models: A Likelihood Approach
- Sensitivity and convergence of uniformly ergodic Markov chains
- A note on geometric ergodicity and floating-point roundoff error
