# Posts tagged case study

## A Primer on Bayesian Methods for Multilevel Modeling

Hierarchical or multilevel modeling is a generalization of regression modeling.

## 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.

## Stochastic Volatility model

Asset prices have time-varying volatility (variance of day over day `returns`). In some periods, returns are highly variable, while in others very stable. Stochastic volatility models model this with a latent volatility variable, modeled as a stochastic process. The following model is similar to the one described in the No-U-Turn Sampler paper, .

## Probabilistic Matrix Factorization for Making Personalized Recommendations

So you are browsing for something to watch on Netflix and just not liking the suggestions. You just know you can do better. All you need to do is collect some ratings data from yourself and friends and build a recommendation algorithm. This notebook will guide you in doing just that!

## NBA Foul Analysis with Item Response Theory

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

## 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

## Estimating parameters of a distribution from awkwardly binned data

Let us say that we are interested in inferring the properties of a population. This could be anything from the distribution of age, or income, or body mass index, or a whole range of different possible measures. In completing this task, we might often come across the situation where we have multiple datasets, each of which can inform our beliefs about the overall population.