Monte Carlo methods

DOI10.1007/s13137-017-0101-zzbMath1395.62053OpenAlexW2773090230MaRDI QIDQ1655965

Karl-Rudolf Koch

Publication date: 10 August 2018

Published in: GEM - International Journal on Geomathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13137-017-0101-z

zbMATH Keywords

Bayesian statisticsGibbs samplerMetropolis algorithmMarkov chain Monte Carlo methodsSIR algorithm

Mathematics Subject Classification ID

Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (1)

Artificial intelligence for determining systematic effects of laser scanners

Uses Software

• BayesDA

Cites Work

• Gibbs sampler for computing and propagating large covariance matrices
• Integration of the Monte Carlo covariance estimation strategy into tailored solution procedures for large-scale least squares problems
• Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms
• Generalized trapezoidal distributions
• Random simulation and GPS decorrelation.
• Gibbs sampler by sampling-importance-resampling
• A Note on the Generation of Random Normal Deviates
• Sampling-Based Approaches to Calculating Marginal Densities
• Outlier..........s
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A simple approach to the generation of uniformly distributed random variables with prescribed correlations
• Equation of State Calculations by Fast Computing Machines
• A Convenient Method for Generating Normal Variables
• Robust Estimation of a Location Parameter
• Monte Carlo strategies in scientific computing
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