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notebook: GLM robust (run with pymc v5) #499

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run with pymc v5
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reshamas Jan 10, 2023
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51 changes: 26 additions & 25 deletions examples/generalized_linear_models/GLM-robust.ipynb
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5 changes: 3 additions & 2 deletions examples/generalized_linear_models/GLM-robust.myst.md
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Expand Up @@ -13,7 +13,7 @@ kernelspec:
(GLM-robust)=
# GLM: Robust Linear Regression

:::{post} August, 2013
:::{post} January 10, 2023
:tags: regression, linear model, robust
:category: beginner
:author: Thomas Wiecki, Chris Fonnesbeck, Abhipsha Das, Conor Hassan, Igor Kuvychko, Reshama Shaikh, Oriol Abril Pla
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- The Student-T distribution has, besides the mean and variance, a third parameter called *degrees of freedom* that describes how much mass should be put into the tails. Here it is set to 1 which gives maximum mass to the tails (setting this to infinity results in a Normal distribution!). One could easily place a prior on this rather than fixing it which I leave as an exercise for the reader ;).
- T distributions can be used as priors as well. See {ref}`GLM-hierarchical`
- How do we test if our data is normal or violates that assumption in an important way? Check out this [great blog post](http://allendowney.blogspot.com/2013/08/are-my-data-normal.html) by Allen Downey.
- How do we test if our data is normal or violates that assumption in an important way? Check out this great blog post, [Probably Overthinking It](http://allendowney.blogspot.com/2013/08/are-my-data-normal.html), by Allen Downey.

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Expand All @@ -209,6 +209,7 @@ There, much better! The outliers are barely influencing our estimation at all be
* Updated by @fonnesbeck in September 2016 (pymc#1378)
* Updated by @chiral-carbon in August 2021 (pymc-examples#205)
* Updated by Conor Hassan, Igor Kuvychko, Reshama Shaikh and [Oriol Abril Pla](https://oriolabrilpla.cat/en/) in 2022
* Rerun using PyMC v5, by Reshama Shaikh, January 2023

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