Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Skewed α-stable distributions for modelling textures
Pattern Recognition Letters
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Bayesian inference for α-stable distributions: A random walk MCMC approach
Computational Statistics & Data Analysis
Non-Gaussian mixture models for detection and estimation in heavy-tailed noise
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
A marked point process for modeling lidar waveforms
IEEE Transactions on Image Processing
Bayesian segmentation of magnetic resonance images using the α-stable distribution
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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Over the last decades, the @a-stable distribution has proved to be a very efficient model for impulsive data. In this paper, we propose an extension of stable distributions, namely mixture of @a-stable distributions to model multimodal, skewed and impulsive data. A fully Bayesian framework is presented for the estimation of the stable density parameters and the mixture parameters. As opposed to most previous work on mixture models, the model order is assumed unknown and is estimated using reversible jump Markov chain Monte Carlo. It is important to note that the Gaussian mixture model is a special case of the presented model which provides additional flexibility to model skewed and impulsive phenomena. The algorithm is tested using synthetic and real data, accurately estimating @a-stable parameters, mixture coefficients and the number of components in the mixture.