Statements (56)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Database_Management_System
|
| gptkbp:bfsLayer |
6
|
| gptkbp:bfsParent |
gptkb:Gen.jl
|
| gptkbp:allows |
can be slow to converge
requires careful tuning may get stuck in local optima requires large sample sizes sensitive to initial conditions |
| gptkbp:applies_to |
gptkb:television_channel
gptkb:software_framework statistical physics computational biology |
| gptkbp:can_be_used_with |
other statistical methods
|
| gptkbp:developed_by |
gptkb:John_von_Neumann
gptkb:Stanislaw_Ulam |
| gptkbp:has_programs |
gptkb:quantum_computing
gptkb:robot climate modeling network analysis artificial neural networks |
| https://www.w3.org/2000/01/rdf-schema#label |
Markov chain Monte Carlo
|
| gptkbp:includes |
Gibbs sampling
Hamiltonian Monte Carlo Metropolis algorithm |
| gptkbp:involves |
Markov chains
random sampling |
| gptkbp:is |
computationally intensive
non-deterministic iterative used in econometrics used in social sciences used in finance used in artificial intelligence used in data science used for optimization problems used in epidemiology used in physics simulations used in genetics a cornerstone of modern statistics a method for generating samples based on the law of large numbers exact sampling method suitable for all problems used in high-dimensional spaces used in image analysis |
| gptkbp:is_used_for |
model complex systems
sampling from probability distributions estimate expectations estimating posterior distributions integrating functions perform hypothesis testing simulate random variables |
| gptkbp:passes_through |
for efficiency
|
| gptkbp:provides |
approximate solutions
|
| gptkbp:requires |
burn-in period
convergence diagnostics |