A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
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A spectral basis truncation in space and quadrature in time is analyzed for approximating fractional stochastic evolution equations, with strong error bounds proved and verified numerically.
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INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
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Spectral approximation of a new class of stochastic fractional evolution equations
A spectral basis truncation in space and quadrature in time is analyzed for approximating fractional stochastic evolution equations, with strong error bounds proved and verified numerically.