paul-buerkner/brms

Conway-Maxwell-Poisson distribution

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#607 opened on Feb 22, 2019

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Description

The Conway-Maxwell-Poisson distribution (https://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution) is a generalization of the Poisson distribution that allows for both over- and underdispersion. There have been some attempts to get this distribution running in Stan, but I haven't seen one that works fast enough for most practical applications. Once we got such an implementation though, I think the distribution would make a very valuable addition to brms.

Contributor guide

Tech stack
None
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.
5
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.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
needs investigation
Prerequisites
Bayesian statisticsStan modelingR programmingProbability distributions
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
15
Research direction
Investigate the Conway Maxwell Poisson distribution (see Wikipedia link in issue). Review previous attempts to implement it in Stan and identify why they were too slow. Focus on developing an efficient sampling or likelihood computation algorithm in Stan. Once a fast implementation is available, integrate it into brms by adding a new family function, following existing patterns in the package. Test on simulated data and compare to standard Poisson.
Conway-Maxwell-Poisson distribution · paul-buerkner/brms#607 | Good First Issue