14 citations · 22 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…