Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

DOI10.1080/10618600.2018.1537928OpenAlexW2903442788WikidataQ62899292 ScholiaQ62899292MaRDI QIDQ91057

Leah South, Ziwen An, David J. Nott, Christopher C. Drovandi, David J. Nott, Ziwen An, L. F. South

Publication date: 19 February 2019

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

Full work available at URL: https://eprints.qut.edu.au/102263/96/102263.pdf

zbMATH Keywords

Markov chain Monte Carlo (MCMC)shrinkage estimatorscovariance matrix estimationapproximate Bayesian computation (ABC)

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (9)

Efficient Bayesian Synthetic Likelihood With Whitening Transformations ⋮ ABC of the future ⋮ Likelihood-free inference by ratio estimation ⋮ Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments ⋮ Unnamed Item ⋮ Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach ⋮ Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations ⋮ BSL ⋮ Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors

Uses Software

• glasso

Cites Work

• Bayesian Synthetic Likelihood
• Sparse inverse covariance estimation with the graphical lasso
• A well-conditioned estimator for large-dimensional covariance matrices
• A comparative review of dimension reduction methods in approximate Bayesian computation
• Mathematical modeling of corneal epithelial wound healing
• Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
• Approximate Bayesian Computation: A Nonparametric Perspective

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