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šŸŽ™ļø Speaker: Maxim Kochurov | ā° Time: 9am PT / 12pm ET / 6pm Berlin

Gaussian Processes are probably the most powerful models you can encounter in Bayesian statistics. To apply them, you first need to get familiar with the basics and first principles. While complicated formulas are awesome, we'll more focus on intuition and possible applications. In this lesson, you'll know

  • what is a kernel
  • how to think about kernel parameters
  • how to make gaussian process hierarchy

And a bonus, we'll lead a coding session where we apply a Gaussian process to analyze unbalanced stratified poll data.

State of Bayes is a series of webinars about advances in practical methods and modeling intuition. The major focus of the webinar series will be on understanding concepts of advanced statistical models and introducing prior knowledge into the loop. This free course will be interesting for Bayesian practitioners who want to deepen their understanding about Bayesian modeling.

Sessions (generally bi-weekly)
The full course is:

  • Session 1ļøāƒ£: Introduction ā–¶ļø L1: VIDEO
  • Session 2ļøāƒ£: Bayesian Thinking ā–¶ļø L2: VIDEO
  • Session 3ļøāƒ£: Hierarchical modeling ā–¶ļø L3: VIDEO
  • Session 4ļøāƒ£: Interpretable Linear Regressions ā–¶ļø L4: VIDEO
  • Session 5ļøāƒ£: Bayesian AB testing ā–¶ļø L5: VIDEO
  • Session 6ļøāƒ£: Gaussian Processes (22th June, 2023)
  • Session 7ļøāƒ£: Gaussian Processes for Time Series

šŸ’¼ About the speaker:

  1. Maxim Kochurov
    Maxim is a core developer of PyMC, a probabilistic programming language. Since the foundation of PyMC Labs he helps to improve complex statistical models and create a reusable solution. Besides strong expertise in Bayesian modeling his background includes economics, software engineering, and large-scale computer vision.

šŸ”— Connect with Maxim:
šŸ‘‰ LinkedIn: https://www.linkedin.com/in/ferrine
šŸ‘‰ Twitter: https://twitter.com/ferrine96
šŸ‘‰ GitHub: https://github.com/ferrine
šŸ‘‰ Website: https://ferrine.github.io

šŸ“– Code of Conduct:
Please note that participants are expected to abide by PyMC's Code of Conduct.

šŸ”— Connecting with PyMC Labs:
šŸ‘„ LinkedIn: https://www.linkedin.com/company/pymc-labs/
🐦 Twitter: https://twitter.com/pymc_labs
šŸŽ„ YouTube: https://www.youtube.com/c/PyMCLabs
šŸ¤ Meetup: https://www.meetup.com/pymc-labs-online-meetup/

šŸ”— Connecting with PyMC Open Source:
šŸ’¬ Q&A/Discussion: https://discourse.pymc.io
šŸ™ GitHub: https://github.com/pymc-devs/pymc
šŸ’¼ LinkedIn: https://www.linkedin.com/company/pymc/mycompany
🐄 Twitter: https://twitter.com/pymc_devs
šŸ“ŗ YouTube: https://www.youtube.com/c/PyMCDevelopers
šŸŽ‰ Meetup: https://www.meetup.com/pymc-online-meetup/

Related topics

Machine Learning
Data Science
New Technology
Applied Statistics
Bayesian Statistics

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