[25] Intuitive Bayesian Modeling and Computation with PyMC in Python (Oriol Abril Pla)

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Oriol Abril Pla: Intuitive Bayesian Modeling and Computation with PyMC in Python

## Key Links
- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2021/25-oriol-pymc3.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/277075016/
- Video: https://youtu.be/6dc7JgR8eI0
- GitHub repo: https://github.com/OriolAbril/pymc3-data_umbrella
- Slides: https://oriolabril.github.io/pymc3-data_umbrella/ (click space to move to next slide, not right arrow, right/left arrows move whole sections)

## Resources
- https://github.com/corriebar/Bayesian-Workflow-with-PyMC
- https://developers.google.com/season-of-docs/
- https://github.com/bambinos/bambi
- Probabilistic Programming and Bayesian Modeling with PyMC3 - Christopher Fonnesbeck: https://youtu.be/M-kBB2I4QlE

## Agenda
00:00 Reshama introduces Data Umbrella
06:55 Oriol begins talk
07:50 Bayesian Paradigm (Data is considered once fixed it has been observed; Model parameters are treated as random)
11:20 Prior information (prior distribution)
12:30 Uncertainty
13:55 Probabilistic programming
14:42 Generative modeling
14:48 Automagical solvers
15:55 Inference algorithms (MCMC: HMC+NUTS, Metropolis, Gibbs); Variational inference: ADVI
16:55 PyMC3
17:58 PyMC version 4 is built on top of aesara. PyMC Version 3 was built on Theano.
19:30 PyMC is extensible
20:30 PyMC is community-driven
23:45 PyMC in practice
23:50 Example 1: Coal mining disasters
30:00 Example 2: Housing prices in Berlin
36:30 Acknowledgments
38:15 Resources, where to go next
40:45 python code example
43:45 Oriol's journey in open source and statistics; was a Google Summer of Code scholar

## Event
In this task we'll give an overview of PyMC, and why you should supercharge your data science skills with probabilistic programming. The talk is organized by layers from more generic to more specific. We will cover the main features of the Bayesian paradigm, then probabilistic progamming, then PyMC and we will finish covering some hands on examples of Bayesian modeling with PyMC.

## About the Speaker
Oriol is a Bayesian statistics and open source software enthusiast. He is also a member of the ArviZ and PyMC teams.

LinkedIn: https://www.linkedin.com/in/oriol-abril-pla-1b9123180/
Twitter: https://twitter.com/OriolAbril
GitHub: https://github.com/OriolAbril

#bayesian #computation #python

Видео [25] Intuitive Bayesian Modeling and Computation with PyMC in Python (Oriol Abril Pla) канала Data Umbrella