Gaussian Process Techniques for Wireless Communications

Computer Science – Information Theory

Scientific paper

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This is a Thesis A report submitted to School of Electrical Engineering & Telecommunications, University of New South Wales, A

Scientific paper

Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Classical solutions such that Kalman filter and Particle filter are introduced in this report. Gaussian processes have been introduced as a non-parametric technique for system estimation from supervision learning. For the thesis project, we intend to propose a new, general methodology for inference and learning in non-linear state-space models probabilistically incorporating with the Gaussian process model estimation.

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