5 citations · 10 across the 7 of their papers we have counts for
8 papers
Transfer Learning using 66 Diseases for Disease Forecasting Applications
Lauren J Beesley, Alexander C Murph, Dave Osthus +1
Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models have shown that synthesizin…
Leveraging Synthetic and Genetic Data to Improve Epidemic Forecasting
Dave Osthus, Alexander C. Murph, Emma E. Goldberg +4
Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improv…
Mapping Incidence and Prevalence Peak Data for SIR Forecasting Applications
Alexander C. Murph, G. Casey Gibson, Lauren J. Beesley +4
Infectious disease modeling and forecasting have played a key role in helping assess and respond to epidemics and pandemics. Recent work has leveraged data on disease peak infectio…
Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations
Alexander C. Murph, Justin D. Strait, Kelly R. Moran +3
Large-scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub-surface transport of particles since direct observation of real world undergr…
Bayes Watch: Bayesian Change-point Detection for Process Monitoring with Fault Detection
Alexander C. Murph, Curtis B. Storlie, Patrick M. Wilson +2
When a predictive model is in production, it must be monitored in real-time to ensure that its performance does not suffer due to drift or abrupt changes to data. Ideally, this is…
Introduction to Generalized Fiducial Inference
Alexander C. Murph, Jan Hannig, Jonathan P. Williams
Fiducial inference was introduced in the first half of the 20th century by Fisher (1935) as a means to get a posterior-like distribution for a parameter without having to arbitrari…
A Geometric Perspective on Bayesian and Generalized Fiducial Inference
Yang Liu, Jan Hannig, Alexander C Murph
Post-data statistical inference concerns making probability statements about model parameters conditional on observed data. When a priori knowledge about parameters is available, p…
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…