The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Introduction to PyMC3 for Bayesian Modeling and Inference
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Introduction to PyMC3 for Bayesian Modeling and Inference
This course is part of Introduction to Computational Statistics for Data Scientists Specialization
Instructor: Dr. Srijith Rajamohan
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3.9
(20 reviews)
Beginner level
Recommended experience
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
1. The PyMC3/ArViz framework for Bayesian modeling and inference
2. Build real-world models using PyMC3 and assess the quality of your models
Skills you'll gain
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Assessments
3 assignments
Taught in English
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This course is part of the Introduction to Computational Statistics for Data Scientists Specialization
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There are 4 modules in this course
This module serves as an introduction to the PyMC3 framework for probabilistic programming. It introduces some of the concepts related to modeling and the PyMC3 syntax. The visualization library ArViz, that is integrated into PyMC3, will also be introduced. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html
What's included
12 videos3 readings1 assignment
This module will teach the basics of using PyMC3 to solve regression and classification problems using PyMC3. It will also show how to deal with outliers in your data and create hierarchical models. Finally, a case study is presented to help apply everything that was learned in Module 1 and 2. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html
What's included
14 videos1 assignment
This module introduces various measures and metrics to assess the quality of the solutions inferred using PyMC3. Hands-on examples are used to illustrate how various methods and visualizations can be used in PyMC3. Finally, a brief overview of how to debug PyMC3 algorithms is provided. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html
What's included
11 videos3 readings1 assignment
This is an ungraded final project. We will utilize everything that has been learned in this course to model the disease dynamics of COVID-19 using a SIR model. Utilizing real-life data, the goal would be to infer the parameters of the SIR model for COVID-19.
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1 plugin
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2.8 (5 ratings)Offered by
Recommended if you're interested in Machine Learning
University of California San Diego
Google Cloud
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