
A Bayesian Hidden SemiMarkov Model with CovariateDependent State Duration Parameters for HighFrequency Environmental Data
Environmental time series data observed at high frequencies can be studi...
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Correcting spatial Gaussian process parameter and prediction variance estimation under informative sampling
Informative sampling designs can impact spatial prediction, or kriging, ...
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A Bayesian Hidden SemiMarkov Model with CovariateDependent State Duration Parameters for HighFrequency Data from Wearable Devices
Data collected by wearable devices in sports provide valuable informatio...
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A multisurrogate higherorder singular value decomposition tensor emulator for spatiotemporal simulators
We introduce methodology to construct an emulator for environmental and ...
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Bayesian Inverse Reinforcement Learning for Collective Animal Movement
Agentbased methods allow for defining simple rules that generate comple...
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On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semiannual harmonics
Statistical methods are required to evaluate and quantify the uncertaint...
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Deep IntegroDifference Equation Models for SpatioTemporal Forecasting
Integrodifference equation (IDE) models describe the conditional depend...
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The utility of a Bayesian Markov model with PólyaGamma sampling for estimating individual behavior transition probabilities from accelerometer classifications
The use of accelerometers in wildlife tracking provides a finescale dat...
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An R Package for SpatioTemporal Change of Support
Spatiotemporal change of support (STCOS) methods are designed for stati...
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Comparison of Deep Neural Networks and Deep Hierarchical Models for SpatioTemporal Data
Spatiotemporal data are ubiquitous in the agricultural, ecological, and...
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SpatioTemporal Models for Big Multinomial Data using the Conditional Multivariate LogitBeta Distribution
We introduce a Bayesian approach for analyzing highdimensional multinom...
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A Hierarchical SpatioTemporal Statistical Model for Physical Systems
In this paper, we extend and analyze a Bayesian hierarchical spatiotemp...
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Hierarchical (Deep) Echo State Networks with Uncertainty Quantification for SpatioTemporal Forecasting
Longlead forecasting for spatiotemporal problems can often entail comp...
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Interpolating Distributions for Populations in Nested Geographies using Publicuse Data with Application to the American Community Survey
Statistical agencies often publish multiple data products from the same ...
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Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in SpatialTemporal Data
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly...
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An Ensemble Quadratic Echo State Network for Nonlinear SpatioTemporal Forecasting
Spatiotemporal data and processes are prevalent across a wide variety o...
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Christopher K. Wikle
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