Posts tagged posterior predictive
Generalized Extreme Value Distribution
- 27 September 2022
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
- 02 September 2022
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 [Huntington-Klein, 2021] and Causal Inference: The Mixtape [Cunningham, 2021] have chapters devoted to difference in differences.
Counterfactual inference: calculating excess deaths due to COVID-19
- 02 July 2022
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
- 02 March 2022
This notebook covers:
Using shared variables (Data container adaptation)
- 16 December 2021
pymc.Data container class wraps the theano shared variable class and lets the model be aware of its inputs and outputs. This allows one to change the value of an observed variable to predict or refit on new data. All variables of this class must be declared inside a model context and specify a name for them.