Corrie Bartelheimer: A Bayesian Workflow with PyMC and ArviZ | PyData Berlin 2019

Speaker: Corrie Bartelheimer Track:PyData Bayesian Modelling has several advantages such as the handling of uncertainty. While the advantages are well known, implementing a Bayesian model can be a bit more involved and some care needs to be taken to check whether the model converged. Recorded at the PyConDE & PyData Berlin 2019 conference. https://pycon.de More details at the conference page: https://de.pycon.org/program/RRLRYG Twitter:   / pydataberlin   Twitter:   / pyconde   00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...

Junpeng Lao: Writing effective bayesian programs using TensorFlow and TFP | PyData Córdoba
▶︎

Junpeng Lao: Writing effective bayesian programs using TensorFlow and TFP | PyData Córdoba

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

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

[41] Intro to Probabilistic Programming with PyMC (Austin Rochford)
▶︎

[41] Intro to Probabilistic Programming with PyMC (Austin Rochford)

Severance — Music To Refine To feat. ODESZA | Apple TV
▶︎

Severance — Music To Refine To feat. ODESZA | Apple TV

Chris Fonnesbeck - Probabilistic Python: An Introduction to Bayesian Modeling with PyMC
▶︎

Chris Fonnesbeck - Probabilistic Python: An Introduction to Bayesian Modeling with PyMC

The Bayesians are Coming to Time Series
▶︎

The Bayesians are Coming to Time Series

Yann LeCun's $1B Bet Against LLMs [Part 1]
▶︎

Yann LeCun's $1B Bet Against LLMs [Part 1]

The Bayesian Workflow: Building a COVID-19 Model, Part 1 (Thomas Wiecki)
▶︎

The Bayesian Workflow: Building a COVID-19 Model, Part 1 (Thomas Wiecki)

Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker
▶︎

Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker

Festival of Genomics Workshop on Agentic Data Science
▶︎

Festival of Genomics Workshop on Agentic Data Science

Probabilistic Programming and Bayesian Modeling with PyMC3 - Christopher Fonnesbeck
▶︎

Probabilistic Programming and Bayesian Modeling with PyMC3 - Christopher Fonnesbeck

Martin Jankowiak - Brief Introduction to Probabilistic Programming
▶︎

Martin Jankowiak - Brief Introduction to Probabilistic Programming

The Strange Math That Predicts (Almost) Anything
▶︎

The Strange Math That Predicts (Almost) Anything

Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!
▶︎

Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!

Aki Vehtari: On Bayesian Workflow
▶︎

Aki Vehtari: On Bayesian Workflow

AlphaFold - The Most Useful Thing AI Has Ever Done
▶︎

AlphaFold - The Most Useful Thing AI Has Ever Done

Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3  | PyData London 2019
▶︎

Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3 | PyData London 2019

Google DeepMind Distinguished Eng (L9): How To Land a Job at a Frontier Lab | Vlad Feinberg
▶︎

Google DeepMind Distinguished Eng (L9): How To Land a Job at a Frontier Lab | Vlad Feinberg

Hierarchical Time Series With Prophet and PyMC (Matthijs Brouns)
▶︎

Hierarchical Time Series With Prophet and PyMC (Matthijs Brouns)

A Bayesian Approach to Media Mix Modeling (Michael Johns & Zhenyu Wang)
▶︎

A Bayesian Approach to Media Mix Modeling (Michael Johns & Zhenyu Wang)