Top Tweets for #PyMCon2020
📌 Check out this video from #PyMCon2020:
▶️ A Bayesian Approach to Media Mix Modeling
👤 Michael Johns & Zhenyu Wang
👥 Discourse Discussion: https://t.co/nsBeHWkgBv
Video: https://t.co/Id7TAGnS4t
#pymc #bayesian #MMM
In case you missed this at #PyMCon2020, check out:
📣Hierarchical Time Series With Prophet and PyMC
👤@MatthijsBrs
▶️https://t.co/q39uCmIogQ
Discourse discussion: https://t.co/v9l6enPs0W
Learn to make long-term predictions for multiple, related, time-series.
#pymc #bayesian
📌 Check out this video from #PyMCon2020:
📣 Bayesian Machine Learning: A PyMC-Centric Introduction
🗣️ @QuanNguyen3010
Discourse discussion: https://t.co/OKVPNsJ5Eb
Video: https://t.co/3iQxIh9VNB
#pymc #python #bayesian
In case you missed this at #PyMCon2020, check out:
📣Posterior Predictive Sampling in PyMC
👤@lucianopaz
Discourse discussion: https://t.co/GJ1cV0V7Es
Luciano explains how posterior predictive sampling is implemented in PyMC.
https://t.co/l9yGUxa45r
#pymc #python #bayesian
📌 Check out this video from #PyMCon2020:
▶️ The Bayesian Workflow: Building a COVID-19 Model (Part 2)
👤 @twiecki
👥 Discourse Discussion: https://t.co/mB8Me1VkB7
In this tutorial we will build a COVID-19 model from scratch.
#pymc #bayesian
https://t.co/6sIjfyzLxv
📌 Check out this video from #PyMCon2020:
▶️ The Bayesian Workflow: Building a COVID-19 Model (Part 2)
👤 @twiecki
👥 Discourse Discussion: https://t.co/mB8Me1VkB7
In this tutorial we will build a COVID-19 model from scratch.
#pymc #bayesian
https://t.co/6sIjfyzLxv
📌 In case you missed this at #PyMCon2020, check out:
▶️ The Bayesian Workflow: Building a COVID-19 Model (Part 1)
👤 @twiecki
👥 Discourse Discussion: https://t.co/mB8Me1VkB7
In this tutorial we will build a COVID-19 model from scratch.
#pymc #bayesian
https://t.co/6sIjfyzLxv
In case you missed this at #PyMCon2020, check out:
• Calibr8: Going Beyond Linear Ranges With Non-Linear Calibration Curves and Multilevel Modeling
• by @theCake & @laurahelleckes
• Discourse discussion: https://t.co/bLUzRTgo5C
Video: https://t.co/y4zvfhzueR
#pymc #bayesian
In case you missed this at #PyMCon2020, check out:
• The Bayesian Zig Zag: Developing and Testing PyMC Models
• by @AllenDowney
• Discourse discussion: https://t.co/gZVoTD3na7
• Video: https://t.co/cRNoWaD1OH
#pymc #python #bayesian
In case you missed this at #PyMCon2020, check out:
• Building an ordered logistic regression model for toxicity prediction
• by @liza_p_semenova
• Discourse discussion: https://t.co/ojgrnV1bCx
• Video: https://t.co/FJk2tCKGDl
#pymc #python #bayesian
In case you missed this at #PyMCon2020, check out:
• Using Hierarchical Multinomial Regression to Predict Elections in Paris Districts
•by @alex_andorra
Discourse discussion: https://t.co/z4OUNg9X7M
Video: https://t.co/6wPrDLTta9
#pymc #bayesian #statistics
In case you missed this at #PyMCon2020, check out:
• Posterior Predictive Sampling in PyMC
• Luciano Paz (@lucianopaz)
Luciano explains how posterior predictive sampling is implemented in PyMC, situations where it fails and how to fix it.
https://t.co/l9yGUxa45r
#pymc #bayesian
In case you missed this at #PyMCon2020, check out:
• The Bayesian Workflow: Building a COVID-19 Model (Part 2)
• Thomas Wiecki(@twiecki)
• Discource discussion: https://t.co/NUEtZRh3B8
In this tutorial, we will build a COVID-19 model from scratch.
#pymc
https://t.co/6sIjfyia8V
These keynotes from #PyMCon2020 are a must-watch
- @fonnesbeck: PyMC: Past,Present and Future
- @violapriesemann: Inferring the spread of SARS-CoV-2 --- and measures to mitigate it
- @avehtari : These are a few of my favorite inference diagnostics
https://t.co/H0taHQH9CH
In case you missed this at #PyMCon2020, check out:
• Hierarchical Time Series With Prophet and PyMC
• Matthijs Brouns (@MatthijsBrs)
• Discourse discussion: https://t.co/v9l6enPs0W
Learn to make long-term predictions for multiple, related, time-series.
https://t.co/q39uCmIogQ
In case you missed this at #PyMCon2020, check out:
• Bayesian Machine Learning: A PyMC-Centric Introduction
• Quan Nguyen(@QuanNguyen3010)
This talk highlights some of the most prominent areas in Bayesian ML from the perspective of statisticians.
https://t.co/3iQxIh9VNB
#pymc
In case you missed this at #PyMCon2020, check out:
• Posterior Predictive Sampling in PyMC
• Luciano Paz (@lucianopaz)
Luciano explains how posterior predictive sampling is implemented in PyMC, situations where it fails and how to fix it.
https://t.co/l9yGUxa45r
#pymc #bayesian
In case you missed this at #PyMCon2020, check out:
• The Bayesian Workflow: Building a COVID-19 Model (Part 2)
• Thomas Wiecki(@twiecki)
• Discource discussion: https://t.co/NUEtZRyEZI
In this tutorial, we will build a COVID-19 model from scratch.
#pymc
https://t.co/6sIjfyzLxv
In case you missed this at #PyMCon2020, check out:
• Hierarchical Time Series With Prophet and PyMC3
• Matthijs Brouns (@MatthijsBrs)
#pymc #python
Learn to make long-term predictions for multiple, related, time-series.
https://t.co/bhffqrr183
In case you missed this at #PyMCon2020, check out:
• The Bayesian Workflow: Building a COVID-19 Model (Part 1)
• Thomas Wiecki (@twiecki)
• Bonus: discussion on Discourse
In this tutorial we will build a COVID-19 model from scratch.
https://t.co/htQ1tA9Uyd
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