11 citations · 25 across the 5 of their papers we have counts for
5 papers
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems
Wai Meng Kwok, Sarat Chandra Dass, George Streftaris
Parameter estimation and associated uncertainty quantification is an important problem in dynamical systems characterized by ordinary differential equation (ODE) models that are of…
Inference for Differential Equation Models using Relaxation via Dynamical Systems
Kyoungjae Lee, Jaeyong Lee, Sarat C. Dass
Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomenons dynamical in nature, which are abun…
Laplace based approximate posterior inference for differential equation models
Sarat C. Dass, Jaeyong Lee, Kyoungjae Lee +1
Ordinary differential equations are arguably the most popular and useful mathematical tool for describing physical and biological processes in the real world. Often, these physical…
A generalized mixed model framework for assessing fingerprint individuality in presence of varying image quality
Sarat C. Dass, Chae Young Lim, Tapabrata Maiti
Fingerprint individuality refers to the extent of uniqueness of fingerprints and is the main criteria for deciding between a match versus nonmatch in forensic testimony. Often, pri…
Hierarchical mixture models for assessing fingerprint individuality
Sarat C. Dass, Mingfei Li
The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures o…