Posts tagged generalized linear model

Bayesian regression with truncated or censored data

The notebook provides an example of how to conduct linear regression when your outcome variable is either censored or truncated.

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Rolling Regression

Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.

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GLM: Negative Binomial Regression

This notebook closely follows the GLM Poisson regression example by Jonathan Sedar (which is in turn inspired by a project by Ian Osvald) except the data here is negative binomially distributed instead of Poisson distributed.

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NBA Foul Analysis with Item Response Theory

This tutorial shows an application of Bayesian Item Response Theory [Fox, 2010] to NBA basketball foul calls data using PyMC. Based on Austin Rochford’s blogpost NBA Foul Calls and Bayesian Item Response Theory.

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Binomial regression

This notebook covers the logic behind Binomial regression, a specific instance of Generalized Linear Modelling. The example is kept very simple, with a single predictor variable.

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GLM: Model Selection

A fairly minimal reproducible example of Model Selection using WAIC, and LOO as currently implemented in PyMC3.

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Hierarchical Binomial Model: Rat Tumor Example

This short tutorial demonstrates how to use PyMC to do inference for the rat tumour example found in chapter 5 of Bayesian Data Analysis 3rd Edition [Gelman et al., 2013]. Readers should already be familiar with the PyMC API.

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GLM: Mini-batch ADVI on hierarchical regression model

Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that.

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