An Individualized Allocation Algorithm for Use with Bayesian Hierarchical Models

Statistical models have widespread use in data science, and while some datasets can be mod- eled well using one model other applications may require multiple models to accurately capture mechanisms within different subgroups of the dataset. Which subjects are assigned to each model in turn impacts the model-level parameters and therefore how well each model fits its subjects, this makes testing all possible subject allocations computationally infeasible. This thesis proposes an algorithm to strategically explore model applications in order to allocate subjects within a feasible compute budget, focusing on allocating individuals’ decision making on a psychological test into one of three different behaviour models.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Data Science, Analytics, and Artificial Intelligence

Grantor dc:publisher

Carleton University

Year dc:date.issued

2026

Author dc:creator

• Fletcher, Karen Julia

Statement dc:rights

• Copyright © 2026 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2026-17179

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/45336