GitHub

GMM-based estimator for complex-valued linear inverse problems.

gmm-estimator provides an estimator for noisy linear observation models using a complex-valued Gaussian mixture model (GMM) prior. The package builds on cplx-gmm for fitting the complex-valued GMM ...
Frontiers

A deep learning latent variable model to identify children with autism through motor abnormalities

Autism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are ...
IEEE

A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions

Abstract: Bayesian networks are powerful statistical models to study the probabilistic relationships among sets of random variables with significant applications in disease modeling and prediction.
C&EN

Gaussian Process Regression for Materials and Molecules

Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom Laboratory of Computational Science and Modeling, IMX, ...
Frontiers

Use of an INLA Latent Gaussian Modeling Approach to Assess Bird Population Changes Due to the Development of Offshore Wind Farms

The utilization of marine renewable energies such as offshore wind farming leads to globally expanding human activities in marine habitats. While knowledge on the responses to offshore wind farms and ...
PNAS

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and ...