Explore probabilistic programming in Python through this 14-minute conference talk from Conf42 Python 2024. Delve into the motivation behind probabilistic programming, compare Bayesian and frequentist statistics, and understand Bayes' theorem. Learn about Bayesian inference, Markov Chain Monte Carlo (MCMC) methods, and probabilistic modeling. Discover the workflow of probabilistic programming and witness a practical demonstration. Gain insights into this powerful approach for handling uncertainty and making data-driven decisions in Python.

Syllabus

intro
preamble
motivation
bayesian vs. frequentist statistics
bayes theorem
bayesian vs non-bayesian inference
bayesian inference
markov chain monte carlo mcmc
probabilistic modelling
workflow of probabilistic programming
demo