Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
Journal of The Royal Society Interface , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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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.