A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet
DOI10.1214/19-AOAS1305zbMath1446.62298arXiv1903.10032OpenAlexW3037601775WikidataQ123398661 ScholiaQ123398661MaRDI QIDQ2194450
Publication date: 26 August 2020
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1903.10032
sequential Monte Carlouncertainty quantificationcomputer model calibrationpaleoclimateAntarctic ice sheet model
Computational methods for problems pertaining to statistics (62-08) Applications of statistics to environmental and related topics (62P12) Monte Carlo methods (65C05) Sequential estimation (62L12) Climate science and climate modeling (86A08)
Uses Software
Cites Work
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- Ensemble samplers with affine invariance
- On particle methods for parameter estimation in state-space models
- On the convergence of adaptive sequential Monte Carlo methods
- Fast dimension-reduced climate model calibration and the effect of data aggregation
- Nonasymptotic analysis of adaptive and annealed Feynman-Kac particle models
- Improving ice sheet model calibration using paleoclimate and modern data
- Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the antarctic ice sheet
- Sequential Monte Carlo on large binary sampling spaces
- Parameter estimation by implicit sampling
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Importance sampling: intrinsic dimension and computational cost
- Computer model validation with functional output
- Dissipative dynamical systems. I: General theory
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
- Bayesian Calibration of Computer Models
- Reducing Communication Costs for Sparse Matrix Multiplication within Algebraic Multigrid
- Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo
- Learning about physical parameters: the importance of model discrepancy
- Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A Case Study for the Navier--Stokes Equations
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Sequential Monte Carlo Samplers
- Fixed-Width Output Analysis for Markov Chain Monte Carlo
- Solution of Nonlinear Stokes Equations Discretized By High-Order Finite Elements on Nonconforming and Anisotropic Meshes, with Application to Ice Sheet Dynamics
- Computer Model Calibration Using High-Dimensional Output
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
- A sequential particle filter method for static models
- Sequential Monte Carlo Methods for Dynamic Systems
- The Multiple-Try Method and Local Optimization in Metropolis Sampling
- Iterative Importance Sampling Algorithms for Parameter Estimation
- Dimension Reduction for Gaussian Process Emulation: An Application to the Influence of Bathymetry on Tsunami Heights
- Parallelizing Sequential Graph Computations
- A construction method for orthogonal Latin hypercube designs
- Particle Filtering for Stochastic Navier--Stokes Signal Observed with Linear Additive Noise
- Modularization in Bayesian analysis, with emphasis on analysis of computer models
- Inferring climate system properties using a computer model
- On the role of interaction in sequential Monte Carlo algorithms
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