{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/45336"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/45336","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"An Individualized Allocation Algorithm for Use with Bayesian Hierarchical Models","abstract":"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. 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