Sequential Monte Carlo learning with hyperparameter adjustments
K. Wada, Kuniaki Yosui, Yohei Nakada, Takashi Matsumoto · 2003
Sequential Monte Carlo scheme is proposed for online Bayesian learning. The proposed scheme adjusts not only parameters for data fitting but adjust hyperparameters online so that the scheme attempts to avoid over fitting in an adaptive manner. The scheme is tested against simple examples and is shown to be functional.