Background: In many domains, scientists build complex simulators of natural phenomena that encode their
\nhypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow,
\nconstrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is
\ncommon practice, resulting in a single parameter setting that minimizes an objective subject to constraints.<\/p>\n
Results: We propose algorithms for post optimization posterior evaluation (POPE) of simulators. The algorithms
\ncompute and visualize all simulations that can generate results of the same or better quality than the optimum,
\nsubject to constraints. These optimization posteriors are desirable for a number of reasons among which are easy
\ninterpretability, automatic parameter sensitivity and correlation analysis, and posterior predictive analysis. Our
\nalgorithms are simple extensions to an existing simulation-based inference framework called approximate Bayesian
\ncomputation. POPE is applied two biological simulators: a fast and stochastic simulator of stem-cell cycling and a slow
\nand deterministic simulator of tumor growth patterns.<\/p>\n
Conclusions: POPE allows the scientist to explore and understand the role that constraints, both on the input and
\nthe output, have on the optimization posterior. As a Bayesian inference procedure, POPE provides a rigorous
\nframework for the analysis of the uncertainty of an optimal simulation parameter setting.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Background: In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common practice, resulting in a single parameter setting […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-443799","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"BMC 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