Markov chain Monte Carlo methods
GPTKB entity
Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:abbreviation |
gptkb:MCMC
|
| gptkbp:basedOn |
Markov chain
|
| gptkbp:category |
gptkb:Monte_Carlo_methods
gptkb:Computational_statistics Markov processes |
| gptkbp:citation |
Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E. (1953), Equation of State Calculations by Fast Computing Machines
Robert, Christian P.; Casella, George (2004), Monte Carlo Statistical Methods W.R. Gilks, S. Richardson, D.J. Spiegelhalter (1996), Markov Chain Monte Carlo in Practice |
| gptkbp:developedBy |
1950s
|
| https://www.w3.org/2000/01/rdf-schema#label |
Markov chain Monte Carlo methods
|
| gptkbp:improves |
adaptive MCMC
parallel tempering sequential Monte Carlo |
| gptkbp:includes |
gptkb:Hamiltonian_Monte_Carlo
gptkb:Metropolis-Hastings_algorithm gptkb:Gibbs_sampling Slice sampling |
| gptkbp:limitation |
computational cost
autocorrelation burn-in period convergence diagnostics slow mixing |
| gptkbp:notableFor |
gptkb:Ising_model
computational chemistry genetics Bayesian inference econometrics statistical physics image analysis machine learning models |
| gptkbp:property |
random walk
ergodicity stationary distribution asymptotic convergence |
| gptkbp:purpose |
numerical integration
approximate inference sampling from probability distributions |
| gptkbp:relatedTo |
gptkb:Monte_Carlo_method
Markov chain |
| gptkbp:requires |
proposal distribution
acceptance probability transition kernel |
| gptkbp:usedIn |
gptkb:machine_learning
Bayesian statistics computational biology computational physics |
| gptkbp:bfsParent |
gptkb:Wolff_algorithm
gptkb:Hybrid_Monte_Carlo |
| gptkbp:bfsLayer |
7
|