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UID:pretalx-scipy-2026-93ERQP@pretalx.com
DTSTART;TZID=CST:20260715T135500
DTEND;TZID=CST:20260715T142500
DESCRIPTION:Modeling and simulation enable iterative hypothesis testing and
  encoding subject matter expertise into reusable tools. While the Python c
 ommunity has a variety of libraries for modeling\, few exist for system dy
 namics\, a paradigm for top-down analysis of material and information flow
 s over time. Reno is an open-source package combining creation\, visualiza
 tion\, and analysis of system dynamics models with techniques for Bayesian
  inference through integration with PyMC\, supporting probability distribu
 tions in system variables and MCMC sampling to produce posterior distribut
 ions based on observed values. This approach enables simulation and refine
 ment of time series models where variables\, policies\, or knowledge are u
 ncertain\, and data/observations are sparse.
DTSTAMP:20260715T021112Z
LOCATION:Memorial Hall
SUMMARY:Reno: Simplifying Application of Bayesian Inference to System Dynam
 ics - Nathan Martindale
URL:https://pretalx.com/scipy-2026/talk/93ERQP/
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