Abstract
Completely random measures (CRMs) form a key ingredient of a wealth of stochastic models, in particular in Bayesian Nonparametrics for defining prior distributions. CRMs can be represented as infinite series of weighted random point masses. A constructive representation due to Ferguson and Klass provides the jumps of the series in decreasing order. This feature is of primary interest when it comes to sampling since it minimizes the truncation error for a fixed truncation level of the series. In this paper we focus on a general class of CRMs, namely the superposed gamma process, which suitably transformed has already been successfully implemented in Bayesian Nonparametrics, and quantify the quality of the approximation in two ways. First, we derive a bound in probability for the truncation error. Second, following [1], we study a moment-matching criterion which consists in evaluating a measure of discrepancy between actual moments of the CRM and moments based on the simulation output. To this end, we show that the moments of this class of processes can be obtained analytically.
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Acknowledgements
The authors are grateful to two anonymous referees for valuable comments and suggestions. Julyan Arbel was a postdoc at Bocconi University and Collegio Carlo Alberto, Italy, when this article was submitted. This work is supported by the European Research Council (ERC) through StG “N-BNP” 306406.
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Arbel, J., Prünster, I. (2017). On the Truncation Error of a Superposed Gamma Process. In: Argiento, R., Lanzarone, E., Antoniano Villalobos, I., Mattei, A. (eds) Bayesian Statistics in Action. BAYSM 2016. Springer Proceedings in Mathematics & Statistics, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-54084-9_2
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