1. A computer-implemented method of performing linear regression to predict data points, the method comprising: receiving a training set of data, wherein the training set of data comprises one selected from the group consisting of audio data, image data, and data describing motion of an object; building a Bayesian linear regression model based on the training set of data, wherein the Bayesian linear regression model specifies that a hyperplane w is conditioned on a scale parameter λ and that there is Gaussian noise in the output data represented by a noise parameter σ, and wherein the ratio of σ and λ is a regularization parameter for w; applying the Bayesian linear regression model to predict data points associated with the training set of data; and outputting the predicted data points.