Scenario-based Bayesian networks for legal evidence

Abstract The paper explores how the construction of Bayesian networks for legal evidence can be assisted by scenario models, especially how spatio-temporal constraints can be transferred from scenario models to Bayesian models. The paper introduces two new methods for constructing scenario-based Bayesian networks: non-Boolean time-nodes with event-states and non-Boolean event-nodes with time-states .

Authors

Institutions

Publication Details

Journal
Artificial Intelligence and Law
Published
2026-05-23
DOI
https://doi.org/10.1007/s10506-026-09522-9
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Scenario-based Bayesian networks for legal evidence

Hylke Jellema, Christian Dahlman
Artificial Intelligence and Law
Bayesian Modeling and Causal Inference
article

Scenario-based Bayesian networks for legal evidence

Hylke Jellema, Christian Dahlman
article en

Abstract

Abstract The paper explores how the construction of Bayesian networks for legal evidence can be assisted by scenario models, especially how spatio-temporal constraints can be transferred from scenario models to Bayesian models. The paper introduces two new methods for constructing scenario-based Bayesian networks: non-Boolean time-nodes with event-states and non-Boolean event-nodes with time-states .

Artificial Intelligence and Law
University of Groningen (NL), Lund University (SE), Utrecht University (NL)
Lunds Universitet
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Bayesian Modeling and Causal Inference
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.