pystra.reliability.monte_carlo.CrudeMonteCarlo#

Use it: User guide · Worked example · Theory

class CrudeMonteCarlo(model, limit_state, *, options=None, point=None, rng=None)[source]#

Bases: MonteCarlo

Crude Monte Carlo simulation (CMC).

With the default sampling density, failure probability is the fraction of simulated points classified as failures [Faber2009]. For n points and n_f failures,

\[\tilde{p}_f = \frac{n_f}{n}.\]

Finite sampling introduces uncertainty; use the returned sampling coefficient of variation to assess it. A shifted or scaled sampling density uses density-ratio weights instead of unweighted counts.

Parameters:

  • model (StochasticModel) – Named physical variables and their joint probability model.

  • limit_state (LimitState) – Physical response evaluated at each sample point.

  • options (SimulationOptions | None) – Sampling, convergence and transformation settings.

  • point (ArrayLike | None) – Center of the sampling density in standard coordinates; the origin by default.

  • rng (int | integer | Sequence[int] | ndarray | SeedSequence | BitGenerator | Generator | None) – Random source; NumPy’s global generator is not used. A seed recreates the same stream on every run, a generator advances its own state, and None draws fresh entropy.

Methods

init_run

Initialize the model's isoprobabilistic transformation.

run

Run the simulation and return a SimulationResult.
run()[source]#

Run the simulation and return a SimulationResult.

Return type:

SimulationResult

init_run()#

Initialize the model’s isoprobabilistic transformation.

Uses the explicit copula or calibrates the legacy Gaussian Nataf correlation. Must be called at the start of every run() method in subclasses.

Return type:

None