# Decompounding discrete distributions: A non-parametric Bayesian approach

## TL;DR

This paper introduces a non-parametric Bayesian method for estimating the jump distribution and intensity of a discretely observed compound Poisson process, with theoretical guarantees and practical applications.

## Contribution

It develops a novel Bayesian approach with MCMC sampling for decompounding discrete distributions, including theoretical contraction results.

## Key findings

- The method performs well on simulated data.
- It outperforms the frequentist plug-in estimator in experiments.
- Posterior contraction rate is established as 1/√n up to a log factor.

## Abstract

Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a non-parametric Bayesian approach to estimate the intensity of the underlying Poisson process and the distribution of the jumps. We provide a MCMC scheme for obtaining samples from the posterior. We apply our method on both simulated and real data examples, and compare its performance with the frequentist plug-in estimator proposed by Buchmann and Gr\"ubel. On a theoretical side, we study the posterior from the frequentist point of view and prove that as the sample size $n\rightarrow\infty$, it contracts around the `true', data-generating parameters at rate $1/\sqrt{n}$, up to a $\log n$ factor.