5 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…
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…
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…