Efficient Bayesian linear models for a large number of observations
Résumé
Bayesian linear models are widely used as efficient approaches for nonparametric function estimation. In this paper, we present a Bayesian method for generating finite-dimensional linear models that can handle large datasets. This method is based on an efficient Markov chain Monte Carlo algorithm. The advantage of this approach is that sampling is performed before conditioning, rather than after. This enables the use of efficient samplers when the prior covariance matrix exhibits special properties, such as being Toeplitz, block-Toeplitz, or sparse. Numerical examples are provided to illustrate the performance of the proposed method.
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