Delayed Acceptance Elliptical Slice Sampling
ABSTRACT Slice sampling is a well‐established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a normalizing constant. The method is based on choosing a new state on a slice, i.e., a (super‐)level set of the target density function. However,...
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| Published in | Proceedings in applied mathematics and mechanics Vol. 25; no. 1 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
01.03.2025
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| Online Access | Get full text |
| ISSN | 1617-7061 1617-7061 |
| DOI | 10.1002/pamm.70005 |
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| Abstract | ABSTRACT
Slice sampling is a well‐established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a normalizing constant. The method is based on choosing a new state on a slice, i.e., a (super‐)level set of the target density function. However, this process may require several evaluations of the target density and, thus, become computationally demanding, particularly, for Bayesian inference with costly likelihoods appearing, e.g., in the Bayesian approach to inverse problems. In this paper we exploit cheap (deterministic) approximations of the density and propose the delayed acceptance version of the elliptical slice sampler. Numerical experiments illustrate the potential benefits in cost reduction of the novel approach. |
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| AbstractList | Slice sampling is a well‐established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a normalizing constant. The method is based on choosing a new state on a slice, i.e., a (super‐)level set of the target density function. However, this process may require several evaluations of the target density and, thus, become computationally demanding, particularly, for Bayesian inference with costly likelihoods appearing, e.g., in the Bayesian approach to inverse problems. In this paper we exploit cheap (deterministic) approximations of the density and propose the delayed acceptance version of the elliptical slice sampler. Numerical experiments illustrate the potential benefits in cost reduction of the novel approach. ABSTRACT Slice sampling is a well‐established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a normalizing constant. The method is based on choosing a new state on a slice, i.e., a (super‐)level set of the target density function. However, this process may require several evaluations of the target density and, thus, become computationally demanding, particularly, for Bayesian inference with costly likelihoods appearing, e.g., in the Bayesian approach to inverse problems. In this paper we exploit cheap (deterministic) approximations of the density and propose the delayed acceptance version of the elliptical slice sampler. Numerical experiments illustrate the potential benefits in cost reduction of the novel approach. |
| Author | Sprungk, Björn Rudolf, Daniel Bitterlich, Kevin |
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| References | 2013; 18 2021; 31 2023; 11 2002; 29 2019; 61 2010; 19 2019; 1 2021; 139 2016; 51 2024; 56 2004; 1 2018; 60 1999; 61 2018; 55 2025; 31 2003; 31 1953; 21 2014; 33 2010; 9 2005; 14 e_1_2_7_9_1 Metropolis N. (e_1_2_7_3_1) 1953; 21 e_1_2_7_8_1 Rudolf D. (e_1_2_7_19_1) 2013; 18 Rudolf D. (e_1_2_7_6_1) 2018; 55 Christen J. A. (e_1_2_7_11_1) 2005; 14 Roberts G. O. (e_1_2_7_15_1) 1999; 61 e_1_2_7_13_1 e_1_2_7_12_1 Lykkegaard M. B. (e_1_2_7_14_1) 2023; 11 Mira A. (e_1_2_7_16_1) 2002; 29 Stuart A. M. (e_1_2_7_2_1) 2010; 19 Łatuszyński K. (e_1_2_7_18_1) 2024; 56 Hasenpflug M. (e_1_2_7_20_1) 2025; 31 Murray I. (e_1_2_7_10_1) 2016; 51 Natarovskii V. (e_1_2_7_17_1) 2021; 31 Roberts G. O. (e_1_2_7_4_1) 2004; 1 Neal R. M. (e_1_2_7_5_1) 2003; 31 Murray I. (e_1_2_7_7_1) 2010; 9 Natarovskii V. (e_1_2_7_21_1) 2021; 139 |
| References_xml | – volume: 51 start-page: 911 year: 2016 end-page: 919 article-title: Pseudo‐Marginal Slice Sampling publication-title: PMLR – volume: 14 start-page: 795 issue: 4 year: 2005 end-page: 810 article-title: Markov Chain Monte Carlo Using an Approximation publication-title: Journal of Computational and Graphical Statistics – volume: 18 start-page: 1 year: 2013 end-page: 8 article-title: Positivity of Hit‐and‐run and Related Algorithms publication-title: Electronic Communications in Probability – volume: 61 start-page: 509 issue: 3 year: 2019 end-page: 545 article-title: Multilevel Markov Chain Monte Carlo publication-title: SIAM Review – volume: 11 start-page: 1 issue: 1 year: 2023 end-page: 30 article-title: Multilevel Delayed Acceptance MCMC publication-title: SIAM/ASA Journal on Uncertainty Quantification – volume: 31 start-page: 806 issue: 2 year: 2021 end-page: 825 article-title: Quantitative Spectral Gap Estimate and Wasserstein Contraction of Simple Slice Sampling publication-title: Annals of Applied Probability – volume: 1 start-page: 103 issue: 2 year: 2019 end-page: 128 article-title: Accelerating Metropolis‐Hastings Algorithms by Delayed Acceptance publication-title: Foundations of Data Science – volume: 29 start-page: 1 issue: 1 year: 2002 end-page: 12 article-title: Efficiency and Convergence Properties of Slice Samplers publication-title: Scandinavian Journal of Statistics – volume: 1 start-page: 20 year: 2004 end-page: 71 article-title: General State Space Markov Chains and MCMC Algorithms publication-title: Probability Surveys – volume: 9 start-page: 541 year: 2010 end-page: 548 article-title: Elliptical Slice Sampling publication-title: PMLR – volume: 33 start-page: 185 year: 2014 end-page: 193 article-title: Approximate Slice Sampling for Bayesian Posterior Inference – volume: 19 start-page: 451 year: 2010 end-page: 559 article-title: Inverse Problems: A Bayesian Perspective publication-title: Acta Numerica – volume: 61 start-page: 643 issue: 3 year: 1999 end-page: 660 article-title: Convergence of Slice Sampler Markov Chains publication-title: Journal of the Royal Statistical Society. Series B – volume: 31 start-page: 705 issue: 3 year: 2003 end-page: 767 article-title: Slice Sampling publication-title: Annals of Statistics – volume: 60 start-page: 223 issue: 2 year: 2018 end-page: 311 article-title: Optimization Methods for Large‐Scale Machine Learning publication-title: SIAM Review – volume: 21 start-page: 1087 year: 1953 end-page: 1092 article-title: Equation of State Calculations by Fast Computing Machines publication-title: Journal of Chemical Physics – volume: 55 start-page: 1186 issue: 4 year: 2018 end-page: 1202 article-title: Comparison of Hit‐and‐Run, Slice Sampler, and Random Walk Metropolis publication-title: Journal of Applied Probability – volume: 56 start-page: 1440 issue: 4 year: 2024 end-page: 1466 article-title: Convergence of Hybrid Slice Sampling via Spectral Gap publication-title: Advances in Applied Probability – volume: 139 start-page: 7969 year: 2021 end-page: 7978 article-title: Geometric Convergence of Elliptical Slice Sampling publication-title: PMLR – volume: 31 start-page: 1377 issue: 2 year: 2025 end-page: 1401 article-title: Reversibility of Elliptical Slice Sampling Revisited publication-title: Bernoulli – ident: e_1_2_7_8_1 – volume: 11 start-page: 1 issue: 1 year: 2023 ident: e_1_2_7_14_1 article-title: Multilevel Delayed Acceptance MCMC publication-title: SIAM/ASA Journal on Uncertainty Quantification doi: 10.1137/22M1476770 – ident: e_1_2_7_9_1 doi: 10.1137/16M1080173 – volume: 14 start-page: 795 issue: 4 year: 2005 ident: e_1_2_7_11_1 article-title: Markov Chain Monte Carlo Using an Approximation publication-title: Journal of Computational and Graphical Statistics doi: 10.1198/106186005X76983 – volume: 51 start-page: 911 year: 2016 ident: e_1_2_7_10_1 article-title: Pseudo‐Marginal Slice Sampling publication-title: PMLR – volume: 31 start-page: 705 issue: 3 year: 2003 ident: e_1_2_7_5_1 article-title: Slice Sampling publication-title: Annals of Statistics – volume: 18 start-page: 1 year: 2013 ident: e_1_2_7_19_1 article-title: Positivity of Hit‐and‐run and Related Algorithms publication-title: Electronic Communications in Probability doi: 10.1214/ECP.v18-2507 – volume: 1 start-page: 20 year: 2004 ident: e_1_2_7_4_1 article-title: General State Space Markov Chains and MCMC Algorithms publication-title: Probability Surveys doi: 10.1214/154957804100000024 – volume: 9 start-page: 541 year: 2010 ident: e_1_2_7_7_1 article-title: Elliptical Slice Sampling publication-title: PMLR – volume: 19 start-page: 451 year: 2010 ident: e_1_2_7_2_1 article-title: Inverse Problems: A Bayesian Perspective publication-title: Acta Numerica doi: 10.1017/S0962492910000061 – volume: 29 start-page: 1 issue: 1 year: 2002 ident: e_1_2_7_16_1 article-title: Efficiency and Convergence Properties of Slice Samplers publication-title: Scandinavian Journal of Statistics doi: 10.1111/1467-9469.00267 – ident: e_1_2_7_12_1 doi: 10.3934/fods.2019005 – volume: 139 start-page: 7969 year: 2021 ident: e_1_2_7_21_1 article-title: Geometric Convergence of Elliptical Slice Sampling publication-title: PMLR – volume: 31 start-page: 1377 issue: 2 year: 2025 ident: e_1_2_7_20_1 article-title: Reversibility of Elliptical Slice Sampling Revisited publication-title: Bernoulli doi: 10.3150/24-BEJ1774 – ident: e_1_2_7_13_1 doi: 10.1137/19M126966X – volume: 31 start-page: 806 issue: 2 year: 2021 ident: e_1_2_7_17_1 article-title: Quantitative Spectral Gap Estimate and Wasserstein Contraction of Simple Slice Sampling publication-title: Annals of Applied Probability doi: 10.1214/20-AAP1605 – volume: 55 start-page: 1186 issue: 4 year: 2018 ident: e_1_2_7_6_1 article-title: Comparison of Hit‐and‐Run, Slice Sampler, and Random Walk Metropolis publication-title: Journal of Applied Probability doi: 10.1017/jpr.2018.78 – volume: 56 start-page: 1440 issue: 4 year: 2024 ident: e_1_2_7_18_1 article-title: Convergence of Hybrid Slice Sampling via Spectral Gap publication-title: Advances in Applied Probability doi: 10.1017/apr.2024.16 – volume: 61 start-page: 643 issue: 3 year: 1999 ident: e_1_2_7_15_1 article-title: Convergence of Slice Sampler Markov Chains publication-title: Journal of the Royal Statistical Society. Series B doi: 10.1111/1467-9868.00198 – volume: 21 start-page: 1087 year: 1953 ident: e_1_2_7_3_1 article-title: Equation of State Calculations by Fast Computing Machines publication-title: Journal of Chemical Physics doi: 10.1063/1.1699114 |
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Slice sampling is a well‐established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a... Slice sampling is a well‐established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a normalizing... |
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| Title | Delayed Acceptance Elliptical Slice Sampling |
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