In this supplementary material, we discuss some further details of our ESS and VB inference (Sections 1 and 2), the computational complexity of our inference procedures (Section 3), and the correlation structure induced by the GPRN model (Section 4). We also discuss multimodality in the GPRN posterior (Section 5), SVLMC, and some background information and notation for Gaussian process regressi...

The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient's brea...

In multi-output regression applications the correlations between the response variables may vary with the input space and can be highly non-linear. Gaussian process regression networks (GPRNs) are flexible and effective models to represent such complex adaptive output dependencies. However, inference in GPRNs is intractable. In this paper we propose two efficient variational inference methods f...

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive dis...

Truly intelligent systems are capable of pattern discovery and extrapolation without human intervention. Bayesian nonparametric models, which can uniquely represent expressive prior information and detailed inductive biases, provide a distinct opportunity to develop intelligent systems, with applications in essentially any learning and prediction task. Gaussian processes are rich distributions ...

One of the most important characteristics in aspect-oriented requirement modeling is effectively dealing with crosscutting concerns. This paper presents a hierarchical GPRN framework for aspect-oriented requirement modeling. The framework breaks requirements into three layers including goal layer, process layer and requirement net layer. Goal layer defines crosscutting concerns from goals, proc...

OBJECTIVE To assess trends in the first two years of prescribing of COX-2-selective non-steroidal anti-inflammatory drugs (C2SNs) by Australian general practitioners. DESIGN Retrospective analysis of deidentified electronic patient records from GPs enrolled in the General Practice Research Network (GPRN). SETTING AND PARTICIPANTS Overall prescription rates for C2SNs and NSAIDs were assessed...

BACKGROUND Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined t...