Use this probability or sampling tool to compute a stated quantity or generate reproducible draws from a precisely parameterized distribution or stochastic model.

Network

Required object input.

Samples

Required integer input.

Seed

Required string input.

Draw reproducible ancestral samples from a Boolean Bayesian network. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Simulation

The resulting simulation returned as an object.

• Results depend on parameterization, support, random source, seed, convergence, burn-in, autocorrelation, proposal quality, normalization, numerical tails, and whether model assumptions match the process. Samples are not exact evidence of convergence.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Check analytic moments or known test cases, use independent chains and diagnostics for Monte Carlo methods, report seed and effective sample size, and inspect tail behavior.

• Rejection Sampler

  Draw reproducible standard-normal samples truncated to an interval using uniform rejection sampling.

• Beta-Binomial Distribution Sampler

  Generate reproducible beta-binomial samples by drawing a probability from a beta distribution for every experiment.

• Binomial Distribution Sampler

  Generate reproducible binomial random samples.