# Posts by Abhipsha Das

## GLM: Negative Binomial Regression

- 26 September 2023

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## GLM: Robust Linear Regression

- 10 January 2023

Duplicate implicit target name: “glm: robust linear regression”.

## Stochastic Volatility model

- 17 June 2022

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, [Hoffman and Gelman, 2014].

## GLM: Model Selection

- 08 January 2022

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

## Dirichlet mixtures of multinomials

- 08 January 2022

This example notebook demonstrates the use of a Dirichlet mixture of multinomials (a.k.a Dirichlet-multinomial or DM) to model categorical count data. Models like this one are important in a variety of areas, including natural language processing, ecology, bioinformatics, and more.

## Dirichlet process mixtures for density estimation

- 16 September 2021

The Dirichlet process is a flexible probability distribution over the space of distributions. Most generally, a probability distribution, \(P\), on a set \(\Omega\) is a [measure](https://en.wikipedia.org/wiki/Measure_(mathematics%29) that assigns measure one to the entire space (\(P(\Omega) = 1\)). A Dirichlet process \(P \sim \textrm{DP}(\alpha, P_0)\) is a measure that has the property that, for every finite disjoint partition \(S_1, \ldots, S_n\) of \(\Omega\),