The authors combine topological data analysis and multi-objective Bayesian inference to achieve practical parameter identifiability and identify simpler rules in an agent-based model of zebrafish patterns.
Enhancing generalizability of model discovery across parameter space with multi- experiment equation learning for biological systems
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Multi-objective Bayesian inference in an agent-based model of zebrafish patterns via topological data analysis
The authors combine topological data analysis and multi-objective Bayesian inference to achieve practical parameter identifiability and identify simpler rules in an agent-based model of zebrafish patterns.