2010/11/03 by Chong Han, Mr. Chong Han, Ido Nevat +9
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Computer network #Computer science #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Gaussian #Gaussian Processes and Bayesian Inference #Gaussian process #Information Theory (cs.IT) #Physics #Process (computing) #Telecommunications #Wireless #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1011.0786
This is a Thesis A report submitted to School of Electrical Engineering & Telecommunications, University of New South Wales, Australia, based on the work done in Semester 2, 2010. Research work is to be continued in Semester 1, 2011
arxiv created 2010/11/03 · openalex publication_date 2010/11/03 · arxiv updated 2010/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.