Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be trained on very large datasets that were out of the reach of previous deployments of EP and has been shown to be competitive with related techniques based on stochastic variational inference. Nevertheless, the memory resources required scale linearly with the dataset size, unlike in variational methods. This is a severe limitation when the number of instances is very large. Here we show that this problem is avoided when stochastic EP is used to train the model

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are probabilistic and non-parametric and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. The focus of this paper is scalable approximate Bayesian learning of these networks. The paper develops a novel and efficient extension of probabilistic backpropagation, a state-of-the-art method for training Bayesian neural networks, that can be used to train DGPs. The new method leverages a recently proposed method for scaling Expectation Propagation, called stochastic Expectation Propagation. The method is able to automatically discover useful input warping, expansion or compression, and it is therefore is a flexible form of Bayesian kernel design. We demonstrate the success of the new method for supervised learning on several real-world datasets, showing that it typically outperforms GP regression and is never much worse

Comparative study of the degree of patient satisfaction in intermittent catheterization with Lofric and polyvinyl chloride catheters

Actas Urol Esp. 2001 Nov-Dec;25(10):725-30. [Comparative study of the degree of patient satisfaction in intermittent catheterization with Lofric and polyvinyl chloride catheters]. [Article in Spanish] López Pereira P, Martínez Urrutia MJ, Lobato L, Rivas S, Jaureguizar Monereo E. SourceUnidad de Urología Infantil, Hospital Universitario La Paz, Madrid. Abstract PURPOSE: To assess the grade of satisfaction in children on intermittent catheterization with the use of LoFric and PVC conventional catheters. MATERIAL AND METHODS: A total of 40 p with experience in CIC were included in this study. An anonymous questionnaire was sent to all patients after 2-months using the LoFric catheter. Patients were divided in 3 groups (bladder augmentation, artificial sphincter, Mitrofanoff) because of major differences in CIC discomfort between these groups. RESULTS: The questionnaire was completed by 87.5% of the patients (35 p). In 86% (30 p) LoFric catheter training was easy or very easy but in 14% (5 p) it was difficult. Four patients had some difficulty during conventional catheter insertion, in 3 (75%) the difficulty disappeared with the use of LoFric catheter. Of the 51% (18 p) who reported some discomfort during the insertion of conventional catheter, 72% said it was eliminated when the LoFric catheter was used. Of 6 p with some discomfort when removing the conventional catheter, 5 (83%) said it disappeared with the new catheter. Th LoFric catheter was favored by 70% of patients because it reduced the discomfort caused by conventional catheters, bladder insertion was easier and smoother, and gel lubrication was not needed. The 17% of patients reported some difficulty dealing with this slippery catheter. CONCLUSIONS: The use of the LoFric catheter could be justified in patients who report with conventional catheters have some discomfort. It can also be recommended in patients with artificial sphincter, bladder augmentation and Mitrofanoff procedure, in whom any complication related to CIC would have serious consequences

Stellar equilibrium configurations of white dwarfs in the f(R,T)f(R,T) gravity

In this work we investigate the equilibrium configurations of white dwarfs in a modified gravity theory, na\-mely, $f(R,T)$ gravity, for which $R$ and $T$ stand for the Ricci scalar and trace of the energy-momentum tensor, respectively. Considering the functional form $f(R,T)=R+2\lambda T$, with $\lambda$ being a constant, we obtain the hydrostatic equilibrium equation for the theory. Some physical properties of white dwarfs, such as: mass, radius, pressure and energy density, as well as their dependence on the parameter $\lambda$ are derived. More massive and larger white dwarfs are found for negative values of $\lambda$ when it decreases. The equilibrium configurations predict a maximum mass limit for white dwarfs slightly above the Chandrasekhar limit, with larger radii and lower central densities when compared to standard gravity outcomes. The most important effect of $f(R,T)$ theory for massive white dwarfs is the increase of the radius in comparison with GR and also $f(R)$ results. By comparing our results with some observational data of massive white dwarfs we also find a lower limit for $\lambda$, namely, $\lambda >- 3\times 10^{-4}$.Comment: To be published in EPJ

Day-ahead allocation of operation reserve in composite power systems with large-scale centralized wind farms

This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system. A two-level model that solves the allocation problem is presented. The upper model allocates operation reserve among subsystems from the economic point of view. In the upper model, transmission constraints of tielines are formulated to represent limited reserve support from the neighboring system due to wind power fluctuation. The lower model evaluates the system on the reserve schedule from the reliability point of view. In the lower model, the reliability evaluation of composite power system is performed by using Monte Carlo simulation in a multi-area system. Wind power prediction errors and tieline constraints are incorporated. The reserve requirements in the upper model are iteratively adjusted by the resulting reliability indices from the lower model. Thus, the reserve allocation is gradually optimized until the system achieves the balance between reliability and economy. A modified two-area reliability test system (RTS) is analyzed to demonstrate the validity of the method.This work was supported by National Natural Science Foundation of China (No. 51277141) and National High Technology Research and Development Program of China (863 Program) (No. 2011AA05A103)

Seabirds and the circulation of Lyme borreliosis bacteria in the North Pacific

Seabirds act as natural reservoirs to Lyme borreliosis spirochetes and may play a significant role in the global circulation of these pathogens. While Borrelia burgdorferi sensu lato (Bbsl) has been shown to occur in ticks collected from certain locations in the North Pacific, little is known about interspecific differences in exposure within the seabird communities of this region. We examined the prevalence of anti-Bbsl antibodies in 805 individuals of nine seabird species breeding across the North Pacific. Seroprevalence varied strongly among species and locations. Murres (Uria spp.) showed the highest antibody prevalence and may play a major role in facilitating Bbsl circulation at a worldwide scale. Other species showed little or no signs of exposure, despite being present in multispecific colonies with seropositive birds. Complex dynamics may be operating in this wide scale, natural hostparasite system, possibly mediated by the host immune system and host specialization of the tick vector

A Geometric Variational Approach to Bayesian Inference

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in L2, and the Fisher-Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the alpha-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback-Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Frechet derivative operators motivated by the geometry of the Hilbert sphere, and examine its properties. Through simulations and real-data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models