arXiv subjects

Julia R. Falconer

Publications and source records attributed to Julia R. Falconer.

3 recordsLinked to original sources

arXiv2025

Utilising Deep Learning to Elicit Expert Uncertainty

Recent work [ 14 ] has introduced a method for prior elicitation that utilizes records of expert decisions to infer a prior distribution. While this method provides a promising approach to eliciting expert uncertainty, it has only been demonstrated using tabular data, which may not entirely represent the information used by experts to make decisions. In this paper, we demonstrate how analysts can adopt a deep learning approach to utilize the method proposed in [14 ] with the actual information experts use. We provide an overview of deep learning models that can effectively model expert decision-making to elicit distributions that capture expert uncertainty and present an example examining the risk of colon cancer to show in detail how these models can be used.

cs.LG↗

arXiv2023

Eliciting Informative Priors by Modelling Expert Decision Making

This article introduces a new method for eliciting prior distributions from experts. The method models an expert decision-making process to infer a prior probability distribution for a rare event $A$. More specifically, assuming there exists a decision-making process closely related to $A$ which forms a decision $Y$, where a history of decisions have been collected. By modelling the data observed to make the historic decisions, using a Bayesian model, an analyst can infer a distribution for the parameters of the random variable $Y$. This distribution can be used to approximate the prior distribution for the parameters of the random variable for event $A$. This method is novel in the field of prior elicitation and has the potential of improving upon current methods by using real-life decision-making processes, that can carry real-life consequences, and, because it does not require an expert to have statistical knowledge. Future decision making can be improved upon using this method, as it highlights variables that are impacting the decision making process. An application for eliciting a prior distribution of recidivism, for an individual, is used to explain this method further.

stat.ME↗

arXiv2022

Methods for Eliciting Informative Prior Distributions: A Critical Review

Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. While popular methods rely on asking experts probability based questions to quantify uncertainty, these methods are not without their drawbacks and many alternative elicitation methods exist. This paper explores methods for eliciting informative priors categorized by type and briefly discusses their strengths and limitations. Most of the review literature in this field focuses on a particular type of elicitation approach. The primary aim of this work, however, is to provide a more complete yet macro view of the state of the art by highlighting new (and old) approaches in one clear, easy to read article. Two representative applications are used throughout to explore the suitability, or lack thereof, of the existing methods; one of which, highlights a challenge that has not been addressed in the literature yet. We identify some of the gaps in the present work and discuss directions for future research.

stat.ME↗