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Iterative importanc...
Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis
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- Raices Cruz, Ivette (author)
- Lund University,Lunds universitet,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Centre for Environmental and Climate Science (CEC),Faculty of Science
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- Lindström, Johan (author)
- Lund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Centre for Environmental and Climate Science (CEC),Faculty of Science,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Troffaes, Matthias C.M. (author)
- Durham University
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- Sahlin, Ullrika (author)
- Lund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Beräkningsvetenskap för hälsa och miljö,Forskargrupper vid Lunds universitet,Centre for Environmental and Climate Science (CEC),Faculty of Science,Computational Science for Health and Environment,Lund University Research Groups
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(creator_code:org_t)
- Elsevier BV, 2022
- 2022
- English.
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In: Computational Statistics and Data Analysis. - : Elsevier BV. - 0167-9473. ; 176
- Related links:
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Abstract
Subject headings
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- Bayesian inference under a set of priors, called robust Bayesian analysis, allows for estimation of parameters within a model and quantification of epistemic uncertainty in quantities of interest by bounded (or imprecise) probability. Iterative importance sampling can be used to estimate bounds on the quantity of interest by optimizing over the set of priors. A method for iterative importance sampling when the robust Bayesian inference relies on Markov chain Monte Carlo (MCMC) sampling is proposed. To accommodate the MCMC sampling in iterative importance sampling, a new expression for the effective sample size of the importance sampling is derived, which accounts for the correlation in the MCMC samples. To illustrate the proposed method for robust Bayesian analysis, iterative importance sampling with MCMC sampling is applied to estimate the lower bound of the overall effect in a previously published meta-analysis with a random effects model. The performance of the method compared to a grid search method and under different degrees of prior-data conflict is also explored.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- Bounds on probability
- Effective sample size
- Meta-analysis
- Random effects model
- Uncertainty quantification
Publication and Content Type
- art (subject category)
- ref (subject category)
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