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Monte Carlo Inference for Alpha-Stable Processes

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In this talk we will present a novel approach to inference in previously intractable alpha-stable stochastic processes. The methods are based on a Poisson sum series representation of the alpha-stable noise process and a modified Poisson sum series representation of the alpha-stable Levy process. Both representations provide a conditionally Gaussian framework, and hence allow for the use of an auxiliary variables Monte Carlo simulation scheme. To overcome the issues due to truncation of the series, residual approximations are developed. Simulations will be given of parameter estimation including model parameters, model order and alpha-stable distribution parameters for discrete-time autoregressive processes driven by alpha-stable innovations.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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