9 citations · 23 across the 12 of their papers we have counts for
13 papers
The RITAS algorithm: a constructive yield monitor data processing algorithm
Luis Damiano, Jarad Niemi
Yield monitor datasets are known to contain a high percentage of unreliable records. The current tool set is mostly limited to observation cleaning procedures based on heuristic or…
Automatic Dynamic Relevance Determination for Gaussian process regression with high-dimensional functional inputs
Luis Damiano, Margaret Johnson, Joaquim Teixeira +2
In the context of Gaussian process regression with functional inputs, it is common to treat the input as a vector. The parameter space becomes prohibitively complex as the number o…
Specifying Prior Distributions in Reliability Applications
Qinglong Tian, Colin Lewis-Beck, Jarad Niemi +1
Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These includ…
Valid predictions of random quantities in linear mixed models
Nicholas Syring, Fernando Miguez, Jarad Niemi
In applications of linear mixed-effects models, experimenters often desire uncertainty quantification for random quantities, like predicted treatment effects for unobserved individ…
Knot Selection in Sparse Gaussian Processes with a Variational Objective Function
Nathaniel Garton, Jarad Niemi, Alicia Carriquiry
Sparse, knot-based Gaussian processes have enjoyed considerable success as scalable approximations to full Gaussian processes. Certain sparse models can be derived through specific…
Knot Selection in Sparse Gaussian Processes
Nathaniel Garton, Jarad Niemi, Alicia Carriquiry
Knot-based, sparse Gaussian processes have enjoyed considerable success as scalable approximations to full Gaussian processes. Problems can occur, however, when knot selection is d…
Score-based likelihood ratios to evaluate forensic pattern evidence
Nathaniel Garton, Danica Ommen, Jarad Niemi +1
In 2016, the European Network of Forensic Science Institutes (ENFSI) published guidelines for the evaluation, interpretation and reporting of scientific evidence. In the guidelines…
Assessing the impacts of time to detection distribution assumptions on detection probability estimation
Adam Martin-Schwarze, Jarad Niemi, Philip Dixon
Abundance estimates from animal point-count surveys require accurate estimates of detection probabilities. The standard model for estimating detection from removal-sampled point-co…
A fully Bayesian strategy for high-dimensional hierarchical modeling using massively parallel computing
Will Landau, Jarad Niemi
Markov chain Monte Carlo (MCMC) is the predominant tool used in Bayesian parameter estimation for hierarchical models. When the model expands due to an increasing number of hierarc…
Bayesian inference for a covariance matrix
Ignacio Alvarez, Jarad Niemi, Matt Simpson
Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. rand…
Massively parallel approximate Gaussian process regression
Robert B. Gramacy, Jarad Niemi, Robin M. Weiss
We explore how the big-three computing paradigms -- symmetric multi-processor (SMC), graphical processing units (GPUs), and cluster computing -- can together be brought to bare on…
Mixture Likelihood Ratio Scan Statistic for Disease Outbreak Detection
Michael D. Porter, Jarad B. Niemi, Brian J. Reich
Early detection of disease outbreaks is of paramount importance to implementing intervention strategies to mitigate the severity and duration of the outbreak. We build methodology…
Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units
Jarad Niemi, Matthew Wheeler
A goal of systems biology is to understand the dynamics of intracellular systems. Stochastic chemical kinetic models are often utilized to accurately capture the stochastic nature…