Non-linear regression based on reproducing kernel Hilbert space (RKHS) has recently become very popular in fitting high-dimensional data. The RKHS formulation provides an automatic dimension reduction of the covariates. This is particularly helpful when the number of covariates ($p$) far exceed the number of data points. In this paper, we introduce a Bayesian nonlinear multivariate regression m...

A regression based method is proposed to recover human body pose from 3D voxel data. In order to do this we need to convert the voxel data into a feature vector. This is done using a Bayesian approach based on Mixture of Probabilistic PCA that transforms a collection of 3D shape context descriptors, extracted from the voxels, to a compact feature vector. For the regression, the newly-proposed M...

This article gives a basic introduction to the principles of Bayesian inference in a machine learning context, with an emphasis on the importance of marginalisation for dealing with uncertainty. We begin by illustrating concepts via a simple regression task before relating ideas to practical, contemporary, techniques with a description of ‘sparse Bayesian’ models and the ‘relevance vector machi...

Using a state-space system, I forecasted the US Treasury yields by employing frequentist and Bayesian methods after first decomposing of varying maturities into its unobserved term structure factors. Then, exploited model to forecast yields, compared performance each using metric - mean squared error, as loss function. Among methods, applied two-step Diebold-Li, principal components, one-step K...