David Higdon
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View article: Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra
Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra Open
Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators to efficiently and effectively predict matter power spectra …
View article: Bayesian "Deep" Process Convolutions: An Application in Cosmology
Bayesian "Deep" Process Convolutions: An Application in Cosmology Open
The nonlinear matter power spectrum in cosmology describes how matter density fluctuations vary with scale in the universe, providing critical insights into large-scale structure formation. The matter power spectrum includes both smooth re…
View article: Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo
Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo Open
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, particularly in real-time or online environments where data arr…
View article: Nonparametric reconstruction of the dark energy equation of state
Nonparametric reconstruction of the dark energy equation of state Open
The major aim of ongoing and upcoming cosmological surveys is to unravel the nature of dark energy. In the absence of a compelling theory to test, a natural approach is to first attempt to characterize the nature of dark energy in detail, …
View article: The Mira–Titan Universe – IV. High-precision power spectrum emulation
The Mira–Titan Universe – IV. High-precision power spectrum emulation Open
Modern cosmological surveys are delivering data sets characterized by unprecedented quality and statistical completeness; this trend is expected to continue in the future as new ground- and space-based surveys come online. In order to maxi…
View article: The Mira-Titan Universe IV. High Precision Power Spectrum Emulation
The Mira-Titan Universe IV. High Precision Power Spectrum Emulation Open
Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness; this trend is expected to continue into the future as new ground- and space-based surveys come online. In order to max…
View article: Analyzing Stochastic Computer Models: A Review with Opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities Open
In modern science, computer models are often used to understand complex\nphenomena, and a thriving statistical community has grown around analyzing\nthem. This review aims to bring a spotlight to the growing prevalence of\nstochastic compu…
View article: Active Learning for Deep Gaussian Process Surrogates
Active Learning for Deep Gaussian Process Surrogates Open
Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning for their nonstationary flexibility and ability to cope with abrupt regime changes in training data. Here, we explore DGPs as surrogates for c…
View article: A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses
A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses Open
This article presents a Bayesian inferential method where the likelihood for a model is unknown but where data can easily be simulated from the model. We discretize simulated (continuous) data to estimate the implicit likelihood in a Bayes…
View article: Stochastic Simulators: An Overview with Opportunities
Stochastic Simulators: An Overview with Opportunities Open
In modern science, deterministic computer models are often used to understand complex phenomena, and a thriving statistical community has grown around effectively analysing them. This review aims to bring a spotlight to the growing prevale…
View article: Analyzing Stochastic Computer Models: A Review with Opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities Open
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer…
View article: Hierarchical Linear Regression (Rev. 1)
Hierarchical Linear Regression (Rev. 1) Open
We describe an algorithm for making inferences in a hierarchical linear model. The model allows different slopes and intercepts for different groups, and ties together the different slopes by representing them as samples from a higher leve…
View article: The Mira-Titan Universe. II. Matter Power Spectrum Emulation
The Mira-Titan Universe. II. Matter Power Spectrum Emulation Open
We introduce a new cosmic emulator for the matter power spectrum covering eight cosmological parameters. Targeted at optical surveys, the emulator provides accurate predictions out to a wavenumber Mpc −1 and redshift . In addition to cover…
View article: Joining statistics and geophysics for assessment and uncertainty quantification of three‐dimensional seismic Earth models
Joining statistics and geophysics for assessment and uncertainty quantification of three‐dimensional seismic Earth models Open
Seismic inversions produce seismic models, which are 3‐dimensional ( 3D ) images of wave velocity of the entire planet retrieved by fitting seismic measurements made on records of past earthquakes or other seismic events. Computing power o…
View article: Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example
Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example Open
Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps and distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. The…
View article: Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example
Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example Open
Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps/distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. These p…
View article: THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS
THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS Open
Large-scale simulations of cosmic structure formation play an important role in interpreting cosmological observations at high precision. The simulations must cover a parameter range beyond the standard six cosmological parameters and need…
View article: Approximate models for the ion-kinetic regime in inertial-confinement-fusion capsule implosions
Approximate models for the ion-kinetic regime in inertial-confinement-fusion capsule implosions Open
“Reduced” (i.e., simplified or approximate) ion-kinetic (RIK) models in radiation-hydrodynamic simulations permit a useful description of inertial-confinement-fusion (ICF) implosions where kinetic deviations from hydrodynamic behavior are …
View article: Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements Open
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to ex…
View article: Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes
Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes Open
Bayesian design of experiments is a methodology for incorporating prior information into the design phase of an experiment. Unfortunately, the typical Bayesian approach to designing experiments is both numerically and analytically intracta…
View article: Validation of Western North America Models based on finite-frequency and ray theory imaging methods
Validation of Western North America Models based on finite-frequency and ray theory imaging methods Open
We validate seismic models developed for western North America with a focus on effect of imaging methods on data fit. We use the DNA09 models for which our collaborators provide models built with both the body-wave FF approach and the RT …
View article: On the validation of seismic imaging methods: Finite frequency or ray theory?
On the validation of seismic imaging methods: Finite frequency or ray theory? Open
We investigate the merits of the more recently developed finite‐frequency approach to tomography against the more traditional and approximate ray theoretical approach for state of the art seismic models developed for western North America.…
View article: Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA
Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA Open
It has become commonplace to use complex computer models to predict outcomes in regions where data do not exist. Typically these models need to be calibrated and validated using some experimental data, which often consists of multiple corr…