Random Sampling from Joint Probability Distributions Defined in a Bayesian Framework
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Publication:4613508
DOI10.1137/18M1168467zbMath1469.62118WikidataQ62563351 ScholiaQ62563351MaRDI QIDQ4613508
Anis Younes, Qian Shao, Marwan Fahs, Thierry Alex Mara
Publication date: 1 February 2019
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Bayesian frameworkdrainage experimentmaximal conditional posterior distributionmodel statistical calibrationposterior uncertainty and sensitivity analyses
Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Monte Carlo methods (65C05)
Uses Software
Cites Work
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- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- Adaptive proposal distribution for random walk Metropolis algorithm
- An introduction to copulas.
- Estimating the dimension of a model
- Estimation of global sensitivity indices for models with dependent variables
- Bayesian sparse polynomial chaos expansion for global sensitivity analysis
- Delayed rejection in reversible jump Metropolis-Hastings
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Sampling-Based Approaches to Calculating Marginal Densities
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- On the Relative Importance of Input Factors in Mathematical Models
- Equation of State Calculations by Fast Computing Machines
- Monte Carlo sampling methods using Markov chains and their applications
- Remarks on a Multivariate Transformation
- A method for the solution of certain non-linear problems in least squares
- An adaptive Metropolis algorithm
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