# Customised Structural Elicitation

arXiv: https://arxiv.org/abs/1807.03693 · HTML: https://mlsanity.com/paper/1807.03693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)

## Abstract (arXiv)

Established methods for structural elicitation typically rely on code modelling standard graphical models classes, most often Bayesian networks. However, more appropriate models may arise from asking the expert questions in common language about what might relate to what and exploring the logical implications of the statements. Only after identifying the best matching structure should this be embellished into a fully quantified probability model. Examples of the efficacy and potential of this more flexible approach are shown below for four classes of graphical models: Bayesian networks, Chain Event Graphs, Multi-regression Dynamic Models, and Flow Graphs. We argue that to be fully effective any structural elicitation phase must first be customised to an application and if necessary new types of structure with their own bespoke semantics elicited.