185 citations · 228 across the 11 of their papers we have counts for
11 papers
Bayesian Adaptive Polynomial Chaos Expansions
Kellin N. Rumsey, Devin Francom, Graham C. Gibson +2
Polynomial chaos expansions (PCE) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparativ…
Elastic Bayesian Model Calibration
Devin Francom, J. Derek Tucker, Gabriel Huerta +2
Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantificatio…
Spatio-temporal extreme event modeling of terror insurgencies
Lekha Patel, Lyndsay Shand, J. Derek Tucker +1
Extreme events with potential deadly outcomes, such as those organized by terror groups, are highly unpredictable in nature and an imminent threat to society. In particular, quanti…
Elastic -means clustering of functional data for posterior exploration, with an application to inference on acute respiratory infection dynamics
Xiao Zang, Sebastian Kurtek, Oksana Chkrebtii +1
We propose a new method for clustering of functional data using a -means framework. We work within the elastic functional data analysis framework, which allows for decomposition…
A Deterministic Hitting-Time Moment Approach to Seed-set Expansion over a Graph
Alexander H. Foss, Richard B. Lehoucq, W. Zachary Stuart +2
We introduce HITMIX, a new technique for network seed-set expansion, i.e., the problem of identifying a set of graph vertices related to a given seed-set of vertices. We use the mo…
Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo
J. Derek Tucker, Lyndsay Shand, Kenny Chowdhary
Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process unce…
Regression Models Using Shapes of Functions as Predictors
Kyungmin Ahn, J. Derek Tucker, Wei Wu +1
Functional variables are often used as predictors in regression problems. A commonly-used parametric approach, called {\it scalar-on-function regression}, uses the $\ltwo$ inner pr…
Computer model calibration based on image warping metrics: an application for sea ice deformation
Yawen Guan, Christian Sampson, J. Derek Tucker +4
Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristic…
Elastic Functional Principal Component Regression
J. Derek Tucker, John Lewis, Anuj Srivastava
We study regression using functional predictors in situations where these functions contain both phase and amplitude variability. In other words, the functions are misaligned due t…
A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data
J. Derek Tucker, John R. Lewis, Caleb King +1
We develop a method for constructing tolerance bounds for functional data with random warping variability. In particular, we define a generative, probabilistic model for the amplit…
Generative Models for Functional Data using Phase and Amplitude Separation
J. Derek Tucker, Wei Wu, Anuj Srivastava
Constructing generative models for functional observations is an important task in statistical functional analysis. In general, functional data contains both phase (or x or horizon…