A new time-domain probabilistic technique based on hierarchical Bayesian modeling (HBM) framework is proposed for calibration and uncertainty quantification of hysteretic type nonlinearities dynamical systems. Specifically, hyper models are introduced respectively material model parameters as well prediction error variance parameters, aiming to consider both the due unmodeled dynamics. asymptot...

This paper addresses an aspect of sign language (SL) recognition that has largely been overlooked in previous work and yet is integral to signed communication. This work is the most comprehensive to-date on the recognition of the complex variations in sign appearances due to grammatical processes (inflections). These processes systematically modulate both the temporal and spatial dimensions of ...

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be im...