Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Database_Management_System
|
| gptkbp:applies_to |
gptkb:television_channel
gptkb:software_framework statistical physics computational biology |
| gptkbp:can_be_used_with |
ensemble methods
deep learning techniques variational inference |
| gptkbp:challenges |
tuning parameters
high autocorrelation slow convergence |
| gptkbp:developed_by |
gptkb:John_von_Neumann
gptkb:Stanislaw_Ulam |
| https://www.w3.org/2000/01/rdf-schema#label |
Markov Chain Monte Carlo
|
| gptkbp:includes |
Metropolis-Hastings algorithm
Gibbs sampling |
| gptkbp:is_criticized_for |
computational intensity
difficulty in implementation lack of theoretical guarantees |
| gptkbp:is_evaluated_by |
Gelman-Rubin diagnostic
Geweke diagnostic autocorrelation plots effective sample size trace plots |
| gptkbp:is_implemented_in |
gptkb:MATLAB
gptkb:R_programming_language gptkb:language gptkb:Julia_programming_language |
| gptkbp:is_popular_in |
gptkb:Artificial_Intelligence
psychometrics data science econometrics |
| gptkbp:is_related_to |
Bayesian inference
stochastic processes random sampling |
| gptkbp:is_supported_by |
Ergodic theory
Monte Carlo integration Markov Chain Central Limit Theorem |
| gptkbp:is_used_for |
sampling from probability distributions
integrating high-dimensional functions optimizing complex models |
| gptkbp:is_used_in |
financial modeling
genetic studies risk assessment image analysis social science research |
| gptkbp:provides |
approximate posterior distributions
|
| gptkbp:related_to |
Markov chains
Monte Carlo methods |
| gptkbp:requires |
burn-in period
convergence diagnostics |
| gptkbp:bfsParent |
gptkb:Pyro
|
| gptkbp:bfsLayer |
3
|