5 papers
Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis
Asir Intesar Tushar, Ioannis Sgouralis
Point-cloud data routinely captured by modern imaging and sensor technologies provide detailed geometric descriptions of objects and environments, but their analysis is hindered by…
Average Kernel Sizes -- Computable Sharp Accuracy Bounds for Inverse Problems
Nina M. Gottschling, David Iagaru, Jakob Gawlikowski +1
The reconstruction of an unknown quantity from noisy measurements is a mathematical problem relevant in most applied sciences, for example, in medical imaging, radar inverse scatte…
Quantitative assessment of biological dynamics with aggregate data
Stephen McCoy, Daniel McBride, D. Katie McCullough +4
We develop and apply a learning framework for parameter estimation in initial value problems that are assessed only indirectly via aggregate data such as sample means and/or standa…
Hamiltonian Monte Carlo methods for spectroscopy data analysis
Daniel McBride, Ioannis Sgouralis
We present a scalable Bayesian framework for the analysis of confocal fluorescence spectroscopy data, addressing key limitations in traditional fluorescence correlation spectroscop…
Inferring effective forces for Langevin dynamics using Gaussian processes
J Shepard Bryan, Ioannis Sgouralis, Steve Pressé
Effective forces -- derived from experimental or {\it in silico} molecular dynamics time traces -- are critical in developing reduced and computationally efficient descriptions of…