# Posts tagged posterior predictive

## Generalized Extreme Value Distribution

The Generalized Extreme Value (GEV) distribution is a meta-distribution containing the Weibull, Gumbel, and Frechet families of extreme value distributions. It is used for modelling the distribution of extremes (maxima or minima) of stationary processes, such as the annual maximum wind speed, annual maximum truck weight on a bridge, and so on, without needing a priori decision on the tail behaviour.

## Difference in differences

This notebook provides a brief overview of the difference in differences approach to causal inference, and shows a working example of how to conduct this type of analysis under the Bayesian framework, using PyMC. While the notebooks provides a high level overview of the approach, I recommend consulting two excellent textbooks on causal inference. Both The Effect and Causal Inference: The Mixtape have chapters devoted to difference in differences.

## Counterfactual inference: calculating excess deaths due to COVID-19

Causal reasoning and counterfactual thinking are really interesting but complex topics! Nevertheless, we can make headway into understanding the ideas through relatively simple examples. This notebook focuses on the concepts and the practical implementation of Bayesian causal reasoning using PyMC.

## Simpson’s paradox and mixed models

This notebook covers:

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`.