Bayesian on-line learning: a sequential Monte Carlo with importance resampling

A Bayesian online learning scheme with sequential Monte Carlo incorporating importance resampling is proposed. The proposed scheme adjusts not only parameters for data fitting but also adjusts hyperparameters online so that the scheme attempts to avoid overfitting in an adaptive manner. One of the advantages of the scheme is the fact that it can adapt to environmental changes, i.e., it can perform learning, even when the underlying input-output relationship varies over time. The scheme is tested against simple examples and is shown to be functional.