Nonparametric Production Technologies with Multiple Component Processes

Nonparametric Independent Component Analysis

Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes

In this paper we propose an efficient, scalable non-parametric Gaussian process model for inference on Poisson point processes. Our model does not resort to gridding the domain or to introducing latent thinning points. Unlike competing models that scale as O(n) over n data points, our model has a complexity O(nk) where k n. We propose a MCMC sampler and show that the model obtained is faster, m...

Nonparametric Mixture of Gaussian Processes with Constraints

Motivated by the need to identify new and clinically relevant categories of lung disease, we propose a novel clustering with constraints method using a Dirichlet process mixture of Gaussian processes in a variational Bayesian nonparametric framework. We claim that individuals should be grouped according to biological and/or genetic similarity regardless of their level of disease severity; there...

Nonparametric Bayesian Density Modeling with Gaussian Processes

The Gaussian process is a useful prior on functions for Bayesian kernel regression and classification. Density estimation with a Gaussian process prior is difficult, however, as densities must be nonnegative and integrate to unity. The statistics community has explored the use of a logistic Gaussian process for density estimation, relying on approximations of the normalization constant (e.g. [1...

Component-Based Software Development with Component Technologies: An Overview

Component-based software development (CBSD) is an approach in which large software systems are built by assembling a set of previously developed software components that can be independently deployed, configured, adapted and connected together within appropriate software architecture. The benefits of this technology include, a shorter development time at a reduced cost with an increased degree ...