Blog - cause it’s better when it’s shared
Hierarchical modeling with the LKJ prior in PyMC
Jun 12, 2022
I describe how to use the `LKJCholeskyCov` and `LKJCorr` distributions
to include correlated priors in Bayesian hierarchical modeling using PyMC.
GSOC 2021: Final evaluation
Aug 17, 2021
Final post about Google Summer of Code 2021.
This post sums up my contributions to the Bambi library during the ten weeks of this program.
This post sums up my contributions to the Bambi library during the ten weeks of this program.
Binomial family in Bambi
Aug 3, 2021
My fourth post describing work done during GSoC 2021. On this
occasion, I'm introducing the Binomial family. This new family is very useful
to build models for binary data when each row in the data set contains the
number of successes and the number of trials instead of the results of
Bernoulli trials.
New families in Bambi
Jul 14, 2021
In this third post about my work during this Google Summer of Code
I describe two families of models recently added. The first one, is the
Student T family, used to make linear regressions more robust. The second,
is the Beta family which can be used to model ratings and proportions.
Robust linear regression in Bambi
Jul 5, 2021
Second post about this Google Summer of Code season.
Today I show some of the problems associated with outliers in linear regression and demonstrate how one can implement a robust linear regression in Bambi.
Today I show some of the problems associated with outliers in linear regression and demonstrate how one can implement a robust linear regression in Bambi.
First weeks of GSoC
Jun 28, 2021
First post of a series about my contributions to Bambi in this Google Summer of Code season.
This post highlights new features related to default priors and priors for group-specific effects.
This post highlights new features related to default priors and priors for group-specific effects.
Design matrices for group-specific effects in formulae and lme4
Jun 8, 2021
Bambi uses the library formulae to automatically construct design
matrices for both common and group-specific effects. This post compares design
matrices for group-specific effects obtained with formulae for a variety of
scenarios involving categorical variables with the ones obtained with the R
package lme4.
Why Bambi?
May 24, 2021
An example comparing how to fit a GLM with Bambi and PyMC3. Here I
attempt to highlight how Bambi can help us to write a Bayesian GLM in a concise
manner, saving us from having to realize error-prone tasks that are sometimes
necessary when directly working with PyMC3.
How to generate bingo cards in R
Nov 3, 2020
A walkthrough the process of understanding how bingo cards are composed
and a set of R functions that let us generate random bingo cards and print them
in a nice looking .pdf output.
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