Bayesian mixture of gaussian processes for data association problem

Younghwan Jeon, Ganguk Hwang

Abstract: Highlights•A probabilistic model using mixture of Gaussian processes with a Bayesian approaches for a data association problem.•The number of hyper-parameters is decreased by using a new EM algorithm, leading to choose better hyper-parameters.•The algorithm gives both an association of observations and an estimation of global mixture weights of latent sources.•We derive a lower bound based on theoretical analysis to estimate the effect of the proposed algorithm.