Utilising Deep Learning to Elicit Expert Uncertainty
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916574788583424 |
|---|---|
| author | Falconer, Julia R. Frank, Eibe Polaschek, Devon L. L. Joshi, Chaitanya |
| author_facet | Falconer, Julia R. Frank, Eibe Polaschek, Devon L. L. Joshi, Chaitanya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Utilising Deep Learning to Elicit Expert Uncertainty Falconer, Julia R. Frank, Eibe Polaschek, Devon L. L. Joshi, Chaitanya Machine Learning Other Statistics 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. |
| title | Utilising Deep Learning to Elicit Expert Uncertainty |
| topic | Machine Learning Other Statistics |
| url | https://arxiv.org/abs/2501.11813 |