A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent components into multiple outputs by an input-dependent mixing matrix. Probit classification and support for multiple observation sets are derived by Variational Bayesian inference. Results on several datasets indicate that the LCGP model can recover the correlations between latent signals while simultaneously achieving state-of-the-art performance. We highlight the latent covariances with an EEG classification dataset where latent brain processes and their couplings simultaneously emerge from the model.Comment: 17 pages, 6 figures; accepted to ACML 201

De-randomizing MCMC dynamics with the diffusion Stein operator

Publisher Copyright: © 2021 Neural information processing systems foundation. All rights reserved.Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions. For example, Langevin dynamics (LD) extracts asymptotically exact samples from a diffusion process because the time evolution of its marginal distributions constitutes a curve that minimizes the KL-divergence via steepest descent in the Wasserstein space. Parallel to LD, Stein variational gradient descent (SVGD) similarly minimizes the KL, albeit endowed with a novel Stein-Wasserstein distance, by deterministically transporting a set of particle samples, thus de-randomizes the stochastic diffusion process. We propose de-randomized kernel-based particle samplers to all diffusion-based samplers known as MCMC dynamics. Following previous work in interpreting MCMC dynamics, we equip the Stein-Wasserstein metric with a fiber-Riemannian Poisson structure, with the capacity of characterizing a fiber-gradient Hamiltonian flow that simulates MCMC dynamics. Such dynamics discretize into generalized SVGD (GSVGD), a Stein-type deterministic particle sampler, with particle updates coinciding with applying the diffusion Stein operator to a kernel function. We demonstrate empirically that GSVGD can de-randomize complicated MCMC dynamics, which combine the advantages of auxiliary momentum variables and Riemannian structure, while maintaining the high sample quality from an interacting particle system.Peer reviewe

<em>Helicobacter pylori</em> Infection and Atrophic Corpus Gastritis on Patients with Intellectual Disability: Challenges in the Clinical Translation of Personalized Medicine

The purpose of this chapter is to clarify the prevalence of Helicobacter pylori infection (HPI) and atrophic corpus gastritis (ACG) in patients with intellectual disability (ID) and review the literature surrounding them. We measured the levels of pepsinogen I, pepsinogen II, gastrin-17b (basal), and Helicobacter pylori antibodies from 243 patients with intellectual disability living in Rinnekoti Research Centre at Lakisto area during 2009–2011. We determined the levels of hemoglobin, mean cell volume (MCV), hematocrit, and the mean amount (MCH) and concentration (MCHC) of red cell hemoglobin, the counts of erythrocytes, leucocytes, and thrombocytes. About 43% had high level of Helicobacter pylori antibodies and 6% ACG. Our results show that Helicobacter pylori infection occurs approximately twice the rate it appears in the normal population. Also, the incidence of ACG was higher among patients with ID than normal population. ID may be a risk of getting the Helicobacter pylori infection (HPI) and ACG. In addition, it was found that the level of thrombocytes was increased in HPI group compared to normal group and decreased in ACG group compared to normal group. This study shows that there is clearly a need to investigate (test) more stomach condition in patients with ID