Abstract
A new class of random probability measures, approximating the well-known normalized generalized gamma (NGG) process, is defined. The new process is built from the representation of the NGG process as a discrete measure, where the weights are obtained by normalization of points of a Poisson process larger than a threshold \(\varepsilon\). Consequently, the new process has an as surely finite number of location points. This process is then considered as the mixing measure in a mixture model for density estimation; we apply it to the popular Galaxy dataset. Moreover, we perform some robustness analysis to investigate the effect of the choice of the hyperparameters.
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Bianchini, I. (2015). A New Finite Approximation for the NGG Mixture Model: An Application to Density Estimation. In: Frühwirth-Schnatter, S., Bitto, A., Kastner, G., Posekany, A. (eds) Bayesian Statistics from Methods to Models and Applications. Springer Proceedings in Mathematics & Statistics, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-16238-6_2
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DOI: https://doi.org/10.1007/978-3-319-16238-6_2
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