Posts by Oriol Abril

A Primer on Bayesian Methods for Multilevel Modeling

Hierarchical or multilevel modeling is a generalization of regression modeling.

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Using a “black box” likelihood function (numpy)

This notebook in part of a set of two twin notebooks that perform the exact same task, this one uses numpy whereas this other one uses Cython

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Using Data Containers

After building the statistical model of your dreams, you’re going to need to feed it some data. Data is typically introduced to a PyMC model in one of two ways. Some data is used as an exogenous input, called X in linear regression models, where mu = X @ beta. Other data are “observed” examples of the endogenous outputs of your model, called y in regression models, and is used as input to the likelihood function implied by your model. These data, either exogenous or endogenous, can be included in your model as wide variety of datatypes, including numpy ndarrays, pandas Series and DataFrame, and even pytensor TensorVariables.

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GLM: Robust Regression using Custom Likelihood for Outlier Classification

Using PyMC for Robust Regression with Outlier Detection using the Hogg 2010 Signal vs Noise method.

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