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1
intro
2
preamble
3
motivation
4
bayesian vs. frequentist statistics
5
bayes theorem
6
bayesian vs non-bayesian inference
7
bayesian inference
8
markov chain monte carlo mcmc
9
probabilistic modelling
10
workflow of probabilistic programming
11
demo
Description:
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.

Probabilistic Programming in Python - Conf42 Python 2024

Conf42
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