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...

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Junpeng Lao: Writing effective bayesian programs using TensorFlow and TFP | PyData Córdoba
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[25] Intuitive Bayesian Modeling and Computation with PyMC in Python (Oriol Abril Pla)
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[41] Intro to Probabilistic Programming with PyMC (Austin Rochford)

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

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

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The Bayesians are Coming to Time Series
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Yann LeCun's $1B Bet Against LLMs [Part 1]

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

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

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Festival of Genomics Workshop on Agentic Data Science

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Probabilistic Programming and Bayesian Modeling with PyMC3 - Christopher Fonnesbeck

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Martin Jankowiak - Brief Introduction to Probabilistic Programming

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The Strange Math That Predicts (Almost) Anything

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

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Aki Vehtari: On Bayesian Workflow

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AlphaFold - The Most Useful Thing AI Has Ever Done

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

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

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Hierarchical Time Series With Prophet and PyMC (Matthijs Brouns)

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