text: 298,17,1111229665547612162,972586737871572997,Shota Gugushvili,New preprint: #Bayesian decompounding for discrete distributions. Joint with Frank van der Meulen and Ester Mariucci. The paper is a nice mixture of theory and practice. #julialang implementation is available on GitHub. <LINK> Computer code and datasets are here: <LINK>,https://arxiv.org/abs/1903.11142,"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. ",Decompounding discrete distributions: A non-parametric Bayesian approach,2,"['New preprint: #Bayesian decompounding for discrete distributions. Joint with Frank van der Meulen and Ester Mariucci. The paper is a nice mixture of theory and practice. #julialang implementation is available on GitHub.\n\n<LINK>', 'Computer code and datasets are here: https://t.co/thnCi9xbG1']",19,03,270