14 citations · 22 across the 13 of their papers we have counts for
14 papers
Constrained Gaussian Random Fields with Continuous Linear Boundary Restrictions for Physics-informed Modeling of States
Yue Ma, Oksana A. Chkrebtii, Stephen R. Niezgoda
Boundary constraints in physical, environmental and engineering models restrict smooth states such as temperature to follow known physical laws at the edges of their spatio-tempora…
Likelihood-free Posterior Density Learning for Uncertainty Quantification in Inference Problems
Rui Zhang, Oksana A. Chkrebtii, Dongbin Xiu
Generative models and those with computationally intractable likelihoods are widely used to describe complex systems in the natural sciences, social sciences, and engineering. Fitt…
Probabilistic size-and-shape functional mixed models
Fangyi Wang, Karthik Bharath, Oksana Chkrebtii +1
The reliable recovery and uncertainty quantification of a fixed effect function in a functional mixed model, for modelling population- and object-level variability in noisily o…
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems
Rui Zhang, Oksana A. Chkrebtii, Dongbin Xiu
Many application areas rely on models that can be readily simulated but lack a closed-form likelihood, or an accurate approximation under arbitrary parameter values. Existing param…
Sequential Bayesian Registration for Functional Data
Yoonji Kim, Oksana A. Chkrebtii, Sebastian A. Kurtek
In many modern applications, discretely-observed data may be naturally understood as a set of functions. Functional data often exhibit two confounded sources of variability: amplit…
Bayesian Numerical Methods for Nonlinear Partial Differential Equations
Junyang Wang, Jon Cockayne, Oksana Chkrebtii +2
The numerical solution of differential equations can be formulated as an inference problem to which formal statistical approaches can be applied. However, nonlinear partial differe…
Bayesian Inference for Polycrystalline Materials
James Matuk, Oksana Chkrebtii, Stephen Niezgoda
Polycrystalline materials, such as metals, are comprised of heterogeneously oriented crystals. Observed crystal orientations are modelled as a sample from an orientation distributi…
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…
Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse and Fragmented Functional Data
James Matuk, Karthik Bharath, Oksana Chkrebtii +1
In many applications, smooth processes generate data that is recorded under a variety of observation regimes, such as dense, sparse or fragmented observations that are often contam…
Inference for stochastic kinetic models from multiple data sources for joint estimation of infection dynamics from aggregate reports and virological data
Yury E. García, Oksana A. Chkrebtii, Marcos A. Capistrán and +1
Influenza and respiratory syncytial virus (RSV) are the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. Medical doctors typically base t…
Automatic Detection and Uncertainty Quantification of Landmarks on Elastic Curves
Justin Strait, Oksana Chkrebtii, Sebastian Kurtek
A population quantity of interest in statistical shape analysis is the location of landmarks, which are points that aid in reconstructing and representing shapes of objects. We pro…
Inference for stochastic kinetic models from multiple data sources for joint estimation of infection dynamics from aggregate reports and virological data
Oksana A. Chkrebtii, Yury E. García, Marcos A. Capistrán +1
Before the current pandemic, influenza and respiratory syncytial virus (RSV) were the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. In…
Transdimensional Approximate Bayesian Computation for Inference on Invasive Species Models with Latent Variables of Unknown Dimension
Oksana A. Chkrebtii, Erin K. Cameron, David A. Campbell +1
Accurate information on patterns of introduction and spread of non-native species is essential for making predictions and management decisions. In many cases, estimating unknown ra…
Bayesian Solution Uncertainty Quantification for Differential Equations
Oksana A. Chkrebtii, David A. Campbell, Ben Calderhead +1
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty c…