| Paper: | MLSP-L3.3 | ||
| Session: | Learning Theory II | ||
| Time: | Thursday, May 18, 10:40 - 11:00 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Bayesian Learning and Modeling | ||
| Title: | Bayesian Inference for Continuous-time ARMA Models Driven by non-Gaussian Levy Processes | ||
| Authors: | Simon Godsill, Gary (Ligong) Yang, University of Cambridge, United Kingdom | ||
| Abstract: | In this paper we present methods for estimating the parameters of a class of non-Gaussian continuous-time stochastic process, the continuous-time autoregressive moving average (CARMA) model driven by symmetric alpha-Stable Levy processes. In this challenging framework we are not able to evaluate the likelihood function directly, and instead we use a disctretized approximation to the likelihood. The parameters are then estimated from this approximating model using a Bayesian Monte Carlo scheme, and employing a Kalman filter to marginalize and sample the trajectory of the state process. An efficient exploration of the parameter space is achieved through a novel reparameterization in terms of an equivalent mechanical system. Simulations demonstrate the potential of the methods. | ||
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