Pages that link to "Item:Q2371188"
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The following pages link to A Bayesian tutorial for data assimilation (Q2371188):
Displaying 50 items.
- Sampling, feasibility, and priors in data assimilation (Q262096) (← links)
- Ensemble Kalman filters for dynamical systems with unresolved turbulence (Q728600) (← links)
- Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models (Q1038446) (← links)
- Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges (Q1622168) (← links)
- Uncertainty quantification for a sailing yacht hull, using multi-fidelity Kriging (Q1646061) (← links)
- A coherent structure approach for parameter estimation in Lagrangian data assimilation (Q1691285) (← links)
- Displacement data assimilation (Q1691769) (← links)
- Emulator-assisted reduced-rank ecological data assimilation for nonlinear multivariate dynamical spatio-temporal processes (Q1731188) (← links)
- Gaussian functional regression for linear partial differential equations (Q1800194) (← links)
- PBDW: a non-intrusive reduced basis data assimilation method and its application to an urban dispersion modeling framework (Q1985176) (← links)
- Reducing sensors for transient heat transfer problems by means of variational data assimilation (Q2023447) (← links)
- p-kernel Stein variational gradient descent for data assimilation and history matching (Q2040686) (← links)
- Resource and grade control model updating for underground mining production settings (Q2040720) (← links)
- Bayesian learning of stochastic dynamical models (Q2077593) (← links)
- Model and data reduction for data assimilation: particle filters employing projected forecasts and data with application to a shallow water model (Q2147287) (← links)
- A comparison of nonlinear extensions to the ensemble Kalman filter. Gaussian anamorphosis and two-step ensemble filters (Q2147573) (← links)
- Information geometry of physics-informed statistical manifolds and its use in data assimilation (Q2162022) (← links)
- Resource model updating for compositional geometallurgical variables (Q2238081) (← links)
- Adaptive step-size selection for state-space probabilistic differential equation solvers (Q2302456) (← links)
- Approximate importance sampling Monte Carlo for data assimilation (Q2371191) (← links)
- Sampling the posterior: an approach to non-Gaussian data assimilation (Q2371192) (← links)
- Reconstruction of unsteady viscous flows using data assimilation schemes (Q2375231) (← links)
- A path integral method for data assimilation (Q2472652) (← links)
- A generalized framework to blend different data assimilation models in Bayesian filter (Q2823673) (← links)
- Adaptive Uncertainty Quantification for Computational Fluid Dynamics (Q2864845) (← links)
- Variational data assimilation using targetted random walks (Q2900440) (← links)
- Comparison of sequential data assimilation methods for the Kuramoto-Sivashinsky equation (Q3401975) (← links)
- Book Reviews (Q4592952) (← links)
- Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder (Q4594056) (← links)
- Mission CO<sub>2</sub>ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide (Q4690936) (← links)
- Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation (Q4957372) (← links)
- Nonlinear Laplacian spectral analysis: capturing intermittent and low‐frequency spatiotemporal patterns in high‐dimensional data (Q4969894) (← links)
- Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter (Q4995119) (← links)
- Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs (Q4997437) (← links)
- On Bayesian data assimilation for PDEs with ill-posed forward problems (Q5089412) (← links)
- Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models (Q5130628) (← links)
- Learning on dynamic statistical manifolds (Q5161017) (← links)
- Probabilistic Forecasting and Bayesian Data Assimilation (Q5179035) (← links)
- Bayesian methods for time‐varying state and parameter estimation in induction machines (Q5743805) (← links)
- Understanding the Ensemble Kalman Filter (Q5884466) (← links)
- Modern statistical methods in oceanography: a hierarchical perspective (Q5965037) (← links)
- Numerical linear algebra in data assimilation (Q6068266) (← links)
- Data assimilation for large‐scale spatio‐temporal systems using a location particle smoother (Q6069053) (← links)
- A statistical overview and perspectives on data assimilation for marine biogeochemical models (Q6069126) (← links)
- Assimilating catchment processes with monitoring data to estimate sediment loads to the Great Barrier Reef (Q6069128) (← links)
- Covariate‐based cepstral parameterizations for time‐varying spatial error covariances (Q6090018) (← links)
- Multivariate spatio‐temporal modelling for assessing Antarctica's present‐day contribution to sea‐level rise (Q6139134) (← links)
- Hierarchical ensemble Kalman methods with sparsity-promoting generalized gamma hyperpriors (Q6194476) (← links)
- A robust computational framework for variational data assimilation of mean flows with sparse measurements corrupted by strong outliers (Q6553824) (← links)
- Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments (Q6621646) (← links)
