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SUMMARY:Bayesian Statistics with Python\, No Resampling Necessary - Charle
 s D Lindsey
DTSTART;TZID=America/Chicago:20230712T160500
DTEND;TZID=America/Chicago:20230712T163500
DTSTAMP:20261007T032032Z
UID:pretalx-2023-TVRHYS@cfp.scipy.org
DESCRIPTION:TensorFlow Probability is a powerful library for statistical a
 nalysis in Python.  Using TensorFlow Probability’s implementation of Bay
 esian methods\, modelers can incorporate prior information and obtain para
 meter estimates and a quantified degree of belief in the results. Resampli
 ng methods like Markov Chain Monte Carlo can also be used to perform Bayes
 ian analysis. As an alternative\, we show how to use numerical optimizatio
 n to estimate model parameters\, and then show how numerical differentiati
 on can be used to get a quantified degree of belief. How to perform simula
 tion in Python to corroborate our results is also demonstrated.
LOCATION:Zlotnik Ballroom
URL:https://cfp.scipy.org/2023/talk/TVRHYS/
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