Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.

Innovative capabilities, entrepreneurial process and the dynamic growth of university spin-outs: A process-oriented study with evidence from China

This research aims to develop a conceptual framework in order to enquire into the dynamic growth process of University Spin-outs (hereafter referred to as USOs) in China, attempting to understand the capabilities configuration that are necessary for the dynamic growth. Based on the extant literature and empirical cases, this study attempts to address the question how do USOs in China build and configure the innovative capabilities to cope with the dynamic growth. This paper aims to contribute to the existing literature by providing a theoretical discussion of the USOs' dynamic entrepreneurial process, by investigating the interconnections between innovation problem-solving and the required configuration of innovative capabilities in four growth phases. Further, it presents a particular interest on the impact to the USOs' entrepreneurial innovation process by the integrative capabilities, in terms of knowledge integration, alliance, venture finance and venture governance. To date, studies that have investigated the dynamic development process of USOs in China and have recognized the heterogeneity of USOs in terms of capabilities that are required for rapid growth still remain sparse. Addressing this research gap will be of great interest to entrepreneurs, policy makers, and venture investors. ©2009 IEEE.

Robust approach to dynamic statistical process control

Statistical Process Control (SPC) technique are well established across a wide range of industries. In particular, the plotting of key steady state variables with their statistical limit against time (Shewart charting) is a common approach for monitoring the normality of production. This paper aims with extending Shewart charting techniques to the quality monitoring of variables driven by uncertain dynamic processes, which has particular application in the process industries where it is desirable to monitor process variables on-line as well as final product. The robust approach to dynamic SPC is based on previous work on guaranteed cost filtering for linear systems and is intended to provide a basis for both a wide application of SPC monitoring and also motivate unstructured fault detection.

A study of the influence of free convection on coagulation process

It was assumed [1, 2] that gravity affects the coagulation process in two ways: free convection, which is hard to be avoided on the ground and sedimentation, which can be greatly reduced by the density-matching method. We present a ground-based experiment set-up to study the influence of convection on the perikinetic coagulation for aqueous polystyrene (PS) dispersions. The turbidity measurement was used to evaluate the relative coagulation rate and convection-driven flows in the solution were checked with a visual-magnification system. The pattern of flow field temperature profile in the sample cell is given. Our experiments show that there was no noticeable difference of coagulation rate observed no matter whether convection flows exist (with the flow speed up to 180 mu m/s) or not.

Observation Of Diffusion Process Of A Water Droplet Immersed In Protein Solution In Microgravity

Pure liquid - liquid diffusion driven by concentration gradients is hard to study in a normal gravity environment since convection and sedimentation also contribute to the mass transfer process. We employ a Mach - Zehnder interferometer to monitor the mass transfer process of a water droplet in EAFP protein solution under microgravity condition provided by the Satellite Shi Jian No 8. A series of the evolution charts of mass distribution during the diffusion process of the liquid droplet are presented and the relevant diffusion coefficient is determined.

Monitoring A Micromechanical Process In Macroscale Carbon Nanotube Films And Fibers

The evaluation of mechanical properties of carbon nanotube (CNT) fibers is inherently difficult. Here, Raman scattering-a generic methodology independent of mechanical measurements-is used to determine the interbundle strength and microscopic failure process for various CNT macroarchitectures. Raman data are used to predict the moduli of CNT films and fibers, and to illustrate the influences of the twisting geometries on the fibers' mechanical performances.

An In-Situ Observation On Initial Aggregation Process Of Colloidal Particles Near Three-Phase Contact Line Of Air, Water And Vertical Substrate

The self-assembling process near the three-phase contact line of air, water and vertical substrate is widely used to produce various kinds of nanostructured materials and devices. We perform an in-situ observation on the self-assembling process in the vicinity of the three phase contact line. Three kinds of aggregations, i.e. particle-particle aggregation, particle-chain aggregation and chain-chain aggregation, in the initial stage of vertical deposition process are revealed by our experiments. It is found that the particle particle aggregation and the particle-chain aggregation can be qualitatively explained by the theory of the capillary immersion force and mirror image force, while the chain-chain aggregation leaves an opening question for the further studies. The present study may provide more deep insight into the self-assembling process of colloidal particles.