Probabilistic Programming in Python

Explore probabilistic programming in Python through this EuroPython 2014 conference talk by Thomas Wiecki. Gain insights into Bayesian statistics and learn how to specify and estimate probabilistic models using PyMC3. Discover the power of next-generation sampling algorithms, intuitive model specification syntax, and just-in-time compilation for efficient large-scale probabilistic modeling. Delve into topics such as machine learning, simulation, maximum likelihood estimation, Markov Chain Monte Carlo sampling, and hierarchical models. Understand how probabilistic programming can be applied across various scientific fields, including cognitive science, data science, and quantitative finance.