Fast and accurate approximation of the full conditional for gamma shape parameters

TL;DR

This paper presents a fast, accurate approximation method for the full conditional distribution of gamma shape parameters, simplifying Bayesian inference in models with many such parameters.

Contribution

It introduces a novel, quick algorithm to approximate the full conditional of gamma shape parameters with a gamma distribution, enhancing computational efficiency.

Findings

The approximation is accurate even with small sample sizes.

The method significantly speeds up inference in models with multiple gamma shape parameters.

The approximation can serve as an effective proposal distribution for Metropolis-Hastings.

Abstract

The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt with by using either Metropolis-Hastings moves, rejection sampling methods, or numerical integration. However, in models with a large number of shape parameters, these existing methods are slower or more complicated than one would like, making them burdensome in practice. It turns out that the full conditional distribution of the gamma shape parameter is well approximated by a gamma distribution, even for small sample sizes, when the prior on the shape parameter is also a gamma distribution. This article introduces a quick and easy algorithm for finding a gamma distribution that approximates the full conditional distribution of the shape parameter. We empirically demonstrate the speed and accuracy of the approximation across a wide range of conditions. If exactness is required, the approximation can be used as a proposal distribution for Metropolis-Hastings.