scholarly journals Estimating Mixture of Gaussian Processes by Kernel Smoothing

2013 ◽  
Author(s):  
Mian Huang ◽  
Runze Li ◽  
Hansheng Wang ◽  
Weixin Yao
Keyword(s):  
Gaussian Processes ◽  
Kernel Smoothing ◽  
Mixture Of Gaussian

scholarly journals Estimating Mixture of Gaussian Processes by Kernel Smoothing

2014 ◽  
Vol 32 (2) ◽  
pp. 259-270 ◽  
Author(s):  
Mian Huang ◽  
Runze Li ◽  
Hansheng Wang ◽  
Weixin Yao
Keyword(s):  
Gaussian Processes ◽  
Kernel Smoothing ◽  
Mixture Of Gaussian

scholarly journals A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

2017 ◽  
Author(s):  
Qikun Xiang ◽  
Jie Zhang ◽  
Ido Nevat ◽  
Pengfei Zhang
Keyword(s):  
Real World ◽  
Gaussian Processes ◽  
Spatial Regression ◽  
Participatory Sensing ◽  
Sensing Applications ◽  
Different Types ◽  
Inaccurate Estimation ◽  
Real World Datasets ◽  
Gp Model ◽  
Mixture Of Gaussian

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

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