3 citations · 12 across the 17 of their papers we have counts for
4 papers · 1 filter
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)
Jimmy Hickey, Jonathan P. Williams, Emily C. Hector
Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another populati…
Beyond time-homogeneity for continuous-time multistate Markov models
Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen +2
Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov…
Generalized Fiducial Inference on Differentiable Manifolds
Alexander C Murph, Jan Hannig, Jonathan P Williams
We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous acros…
A penalized complexity prior for deep Bayesian transfer learning with application to materials informatics
Mohamed A. Abba, Jonathan P Williams, Brian J Reich
A key task in the emerging field of materials informatics is to use machine learning to predict a material's properties and functions. A fast and accurate predictive model allows r…