Waley W. J. Liang and Herbert K. H. Lee
07/28/2012 10:45 AM
Applied Mathematics & Statistics
The process convolution approach for constructing a Gaussian Process (GP) model is a computationally efficient approach for larger datasets in lower dimensions. Bayesian inference or speci fically, Markov chain Monte Carlo, is commonly used for estimating the parameters of this model. Applying this model to sequential applications where data arrives on-line requires rerunning the Markov chain for each new data arrival, which can be time consuming. This paper presents an on-line inference method for the process convolution GP model based on a Sequential Monte Carlo method called Particle Learning. This model is illustrated on a synthetic example and an optimization problem in hydrology.
