Elicitation of multivariate prior distributions: A nonparametric Bayesian approach

Moala, Fernando Antônio; O'Hagan, Anthony

doi:10.1016/j.jspi.2010.01.004

Cited by 21 publications

(17 citation statements)

References 12 publications

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“…A more flexible method that avoids experts' belief to fit 815 a pre-specified parametric family is presented in Moala & O'Hagan (2010). While the focus of the elicitation is laid on the analyst who seeks to identify the probability density function for a multivariate vector, the posterior distribution is based on the prior distribution as specified by an 820 expert.…”

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confidence: 99%

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Werner

Bedford

Cooke

et al. 2017

European Journal of Operational Research

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Many applications in decision making under uncertainty and probabilistic risk assessment require the assessment of multiple, dependent uncertain quantities, so that in addition to marginal distributions, interdependence needs to be modelled in order to properly understand the overall risk. Nevertheless, relevant historical data on dependence information are often not available or simply too costly to obtain. In this case, the only sensible option is to elicit this uncertainty through the use of expert judgements. In expert judgement studies, a structured approach to eliciting variables of interest is desirable so that their assessment is methodologically robust. One of the key decisions during the elicitation process is the form in which the uncertainties are elicited. This choice is subject to various, potentially conflicting, desiderata related to e.g. modelling convenience, coherence between elicitation parameters and the model, combining judgements, and the assessment burden for the experts. While extensive and systematic guidance to address these considerations exists for single variable uncertainty elicitation, for higher dimensions very little such guidance is available. Therefore this paper offers a systematic review of the current literature on eliciting dependence. The literature on the elicitation of dependence parameters such as correlations is presented alongside commonly used dependence models and experience from case studies. From this, guidance about the strategy for dependence assessment is given and gaps in the existing research are identified to determine future directions for structured methods to elicit dependence. (Anca M. Hanea), O.MoralesNapoles@tudelft.nl (Oswaldo Morales-Nápoles) cause variables in the model are correlated, or indirectly when an uncertainty analysis of model parameters is carried out to explore model robustness. Both cases exhibit complex interrelations and dependencies which need to be considered if assumptions such as independence are not 10 justifiable.However, it is often not straightforward to either model or quantify dependence. In particular whenever no relevant historical data are available, the only sensible way to achieve uncertainty quantification is through eliciting ex-15

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mentioning

confidence: 99%

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Werner

Bedford

Cooke

et al. 2017

European Journal of Operational Research

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“…However, specifying multivariate prior distributions is not trivial, especially in case of different marginal densities (in our case study, lognormal, beta, and inverse‐gamma distributions were used). In this respect, approaches such as Bayesian nonparametric techniques may be employed . Nevertheless, tackling the high dimensionality would be challenging and will be object of further research.…”

Section: Discussionmentioning

confidence: 99%

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Gamba

Jacobs

Geys

et al. 2019

Pharmaceutical Statistics

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The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK‐PD) framework. While, at first sight, a Bayesian PK‐PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often‐overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

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“…( 29 ) Methods to elicit multivariate prior distributions is also an active field of present research. ( 30 ) Fifteen other techniques to determine priors can be found in Berger. ( 27 )…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…(29) Methods to elicit multivariate prior distributions is also an active field of present research. (30) Fifteen other techniques to determine priors can be found in Berger. (27) In the two application studies, previous available information including expertise was used to encode the prior probabilistic bets for the parameters of the general model only (i.e., the model in which all the other models are nested; see Sections 3.1.2 and 3.2.2 for application).…”

Section: Prior Settingmentioning

confidence: 99%

Hierarchical Bayesian Models to Assess Between‐ and Within‐Batch Variability of Pathogen Contamination in Food

Commeau¹,

Cornu²,

Albert³

et al. 2011

Risk Analysis

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Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.

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Elicitation of multivariate prior distributions: A nonparametric Bayesian approach

Cited by 21 publications

References 12 publications

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Hierarchical Bayesian Models to Assess Between‐ and Within‐Batch Variability of Pathogen Contamination in Food

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