All Outputs (6)

Decision making under severe uncertainty on a budget (2022)
Conference Proceeding
Nakharutai, N., Destercke, S., & Troffaes, M. C. (2022). Decision making under severe uncertainty on a budget. In F. Dupin de Saint-Cyr, M. Öztürk-Escoffier, & N. Potyka (Eds.), . https://doi.org/10.1007/978-3-031-18843-5_13

Convex sets of probabilities are general models to describe and reason with uncertainty. Moreover, robust decision rules defined for them enable one to make cautious inferences by allowing sets of optimal actions to be returned, reflecting lack of in... Read More about Decision making under severe uncertainty on a budget.

Foundations for temporal reasoning using lower previsions without a possibility space (2022)
Book Chapter
Troffaes, M. C., & Goldstein, M. (2022). Foundations for temporal reasoning using lower previsions without a possibility space. In T. Augustin, F. Gagliardi Cozman, & G. Wheeler (Eds.), Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld (69-96). (1). Springer Verlag. https://doi.org/10.1007/978-3-031-15436-2_4

We introduce a new formal mathematical framework for probability theory, taking random quantities to be the fundamental objects of interest, without reference to a possibility space, in spirit of de Finetti’s treatment of probability, Goldstein’s Bay... Read More about Foundations for temporal reasoning using lower previsions without a possibility space.

Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis (2022)
Journal Article
Raices Cruz, I., Lindström, J., Troffaes, M. C., & Sahlin, U. (2022). Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis. Computational Statistics & Data Analysis, 176, Article 107558. https://doi.org/10.1016/j.csda.2022.107558

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 impor... Read More about Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis.

A robust Bayesian analysis of variable selection under prior ignorance (2022)
Journal Article
Basu, T., Troffaes, M. C., & Einbeck, J. (2023). A robust Bayesian analysis of variable selection under prior ignorance. Sankhya A - Mathematical Statistics and Probability, 85(1), 1014-1057. https://doi.org/10.1007/s13171-022-00287-2

We propose a cautious Bayesian variable selection routine by investigating the sensitivity of a hierarchical model, where the regression coefficients are specified by spike and slab priors. We exploit the use of latent variables to understand the imp... Read More about A robust Bayesian analysis of variable selection under prior ignorance.

A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis (2022)
Journal Article
Raices Cruz, I., Troffaes, M. C., Lindström, J., & Sahlin, U. (2022). A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis. Statistics in Medicine, 41(17), 3365-3379. https://doi.org/10.1002/sim.9422

Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network mo... Read More about A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis.

A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments (2022)
Journal Article
Raices Cruz, I., Troffaes, M., & Sahlin, U. (2022). A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments. Risk Analysis, 42(2), 239-253. https://doi.org/10.1111/risa.13871

An honest communication of uncertainty about quantities of interest enhances transparency in scientific assessments. To support this communication, risk assessors should choose appropriate ways to evaluate and characterize epistemic uncertainty. A fu... Read More about A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments.