4 papers
Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
Yu Wang, Arnab Ganguly
Stochastic differential equations (SDEs) provide a flexible framework for modeling temporal dynamics in partially observed systems. A central task is to calibrate such models from…
Stochastic Averaging and Statistical Inference of Glycolytic Pathway
Arnab Ganguly, Hye-Won Kang
Many biological processes exhibit oscillatory behavior. Among these, glycolytic oscillations have been extensively studied due to their well-characterized biochemical reaction netw…
Nonparametric learning of stochastic differential equations from sparse and noisy data
Arnab Ganguly, Riten Mitra, Jinpu Zhou
The paper proposes a systematic framework for building data-driven stochastic differential equation (SDE) models from sparse, noisy observations. Unlike traditional parametric appr…
Nonparametric learning of covariate-based Markov jump processes using RKHS techniques
Yuchen Han, Arnab Ganguly, Riten Mitra
We propose a novel nonparametric approach for linking covariates to Continuous Time Markov Chains (CTMCs) using the mathematical framework of Reproducing Kernel Hilbert Spaces (RKH…