# Estimating an Inverse Gamma distribution

arXiv: https://arxiv.org/abs/1605.01019 · HTML: https://mlsanity.com/paper/1605.01019/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2016.

## Built on by (most cited first, 1 of 1)

- [A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations](https://mlsanity.com/paper/2204.07352/) (2022)

## Abstract (arXiv)

In this paper we introduce five different algorithms based on method of moments, maximum likelihood and full Bayesian estimation for learning the parameters of the Inverse Gamma distribution. We also provide an expression for the KL divergence for Inverse Gamma distributions which allows us to quantify the estimation accuracy of each of the algorithms. All the presented algorithms are novel. The most relevant novelties include the first conjugate prior for the Inverse Gamma shape parameter which allows analytical Bayesian inference, and two very fast algorithms, a maximum likelihood and a Bayesian one, both based on likelihood approximation. In order to compute expectations under the proposed distributions we use the Laplace approximation. The introduction of these novel Bayesian estimators opens the possibility of including Inverse Gamma distributions into more complex Bayesian structures, e.g. variational Bayesian mixture models. The algorithms introduced in this paper are computationally compared using synthetic data and interesting relationships between the maximum likelihood and the Bayesian approaches are derived.