Negative Binomial–Poisson Mixture Sampler

Draw a gamma-distributed Poisson rate and then an exact Poisson count; marginal counts follow the negative-binomial gamma-Poisson mixture in the supported rate range.

Description

Draw a gamma-distributed Poisson rate and then an exact Poisson count; marginal counts follow the negative-binomial gamma-Poisson mixture in the supported rate range.

Negative Binomial–Poisson Mixture Sampler: Draw a gamma-distributed Poisson rate and then an exact Poisson count; marginal counts follow the negative-binomial gamma-Poisson mixture in the supported rate range.

When to use Negative Binomial–Poisson Mixture Sampler

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

shape
Required number input.
success Probability
Required number input.
count
Required integer input.
seed
Required string input.

How Negative Binomial–Poisson Mixture Sampler works

Draw a gamma-distributed Poisson rate and then an exact Poisson count; marginal counts follow the negative-binomial gamma-Poisson mixture in the supported rate range. 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

rates
The resulting rates returned as a list.
samples
The resulting samples returned as a list.

Limitations and assumptions

  • 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.

Alternative or Complementary approaches

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.

References

  1. Probability distribution — Wikipedia contributors

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