Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models
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Publication:5130628
DOI10.1080/01621459.2019.1592753zbMath1445.62246arXiv1704.06988OpenAlexW3102649529WikidataQ128185009 ScholiaQ128185009MaRDI QIDQ5130628
Jonathan R. Stroud, Matthias Katzfuss, Christopher K. Wikle
Publication date: 28 October 2020
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1704.06988
Gibbs samplerstate-space modelsdata assimilationparticle filterspatio-temporal statisticsgeoscience applications
Directional data; spatial statistics (62H11) Inference from stochastic processes and prediction (62M20) Geostatistics (86A32)
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Uses Software
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- The pseudo-marginal approach for efficient Monte Carlo computations
- Comment on article by Dawid and Musio
- Ensemble Kalman filtering with shrinkage regression techniques
- On the convergence of the ensemble Kalman filter.
- Bayesian forecasting and dynamic models.
- Approximate Bayesian computational methods
- The ensemble Kalman filter is an ABC algorithm
- Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
- Gibbs posterior for variable selection in high-dimensional classification and data mining
- Rates of convergence for minimum contrast estimators
- A hierarchical Bayes ensemble Kalman filter
- Coherent dispersion criteria for optimal experimental design
- A dynamic nonstationary spatio-temporal model for short term prediction of precipitation
- State and parameter estimation in stochastic dynamical models
- A Bayesian tutorial for data assimilation
- Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter
- Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
- On the measurability and consistency of minimum contrast estimates
- Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
- Sequential Monte Carlo Methods in Practice
- Deterministic Mean-Field Ensemble Kalman Filtering
- Uncertainty Quantification in the Ensemble Kalman Filter
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Training Products of Experts by Minimizing Contrastive Divergence
- A kernel-based spectral model for non-Gaussian spatio-temporal processes
- Bayes Linear Statistics
- Sampling-Based Approaches to Calculating Marginal Densities
- Practical Filtering with Sequential Parameter Learning
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Accurate Approximations for Posterior Moments and Marginal Densities
- Markov chain Monte Carlo for dynamic generalised linear models
- Venezuelan Rainfall Data Analysed by Using a Bayesian Space–time Model
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- On Gibbs sampling for state space models
- Likelihood analysis of non-Gaussian measurement time series
- Dynamic Conditional Independence Models and Markov Chain Monte Carlo Methods
- Mixture Kalman Filters
- Sequential Monte Carlo Methods for Dynamic Systems
- Marginal Likelihood From the Metropolis–Hastings Output
- An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation
- Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
- Robust Data Assimilation Using $L_1$ and Huber Norms
- Justifying and Generalizing Contrastive Divergence
- High‐Dimensional Covariance Estimation
- Evaluation of likelihood functions for Gaussian signals
- Ensemble Kalman filters for large geophysical applications
- Sequential Monte Carlo without likelihoods
- Note on the Consistency of the Maximum Likelihood Estimate
- Understanding the Ensemble Kalman Filter
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