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- W2160150819 abstract "Alpha stable distributions are widely accepted models for impulsive data. Despite their flexibility in modelling varying degrees of impulsiveness and skewness, they fall short of modelling multimodal data. In this work, we present the alpha-stable mixture model which provides a framework for modelling multimodal, skewed and impulsive data. We describe new parameter estimation techniques for this model based on numerical Bayesian techniques which not only can estimate the alpha-stable and mixture parameters, but also the number of components in the mixture. In particular, we employ the reversible jump Markov chain Monte Carlo technique." @default.
- W2160150819 created "2016-06-24" @default.
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- W2160150819 date "2006-09-04" @default.
- W2160150819 modified "2023-09-22" @default.
- W2160150819 title "Bayesian estimation of mixtures of skewed alpha stable distributions with an unknown number of components" @default.
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- W2160150819 doi "https://doi.org/10.5281/zenodo.53388" @default.
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