An Adaptive Derivative Free Method for Bayesian Posterior Approximation

作者：Raitoharju Matti^*; Ali Loytty Simo

来源：IEEE Signal Processing Letters, 2012, 19(2): 87-90.

DOI：10.1109/LSP.2011.2179800

• Bayesian methods
• Kalman filters
• Gaussian mixture
• nonlinear systems
• unscented transform

In the Gaussian mixture approach a Bayesian posterior probability distribution function is approximated using a weighted sum of Gaussians. This work presents a novel method for generating a Gaussian mixture by splitting the prior taking the direction of maximum nonlinearity into account. The proposed method is computationally feasible and does not require analytical differentiation. Tests show that the method approximates the posterior better with fewer Gaussian components than existing methods.

• 出版日期2012-2