paul-buerkner/brms

Implementing the Skewed T-Distribution

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#513 opened on Sep 3, 2018

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Description

The skew normal family is great for modeling skewed gaussian data, and the student t distribution is great for outliers. But if one has both skewed data and outliers, the skewed t-distribution might be quite useful! How practical would it be to implement this in brms? This paper outlines an implementation in an MLE/EM setting, but I'm not quite skilled enough to implement it in Stan myself.

https://en.wikipedia.org/wiki/Skewed_generalized_t_distribution#Skewed_t_distribution

Contributor guide

Research direction
Read the referenced paper on skewed t distribution. Review how brms defines families and their Stan code, e.g., the skew normal family. Implement the skewed t distribution in Stan, then create the brms family function and tests. Follow the contribution guidelines.
Tech stack
r
Domain
datamachine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Over 1 week
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
RBayesian statistics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
20