celeritas::RejectionSampler< RealType > Class Template Reference

Return whether a rejection loop needs to continue trying. More...

#include <RejectionSampler.hh>

Public Types
Type aliases
using	real_type = RealType
using	result_type = real_type

Public Member Functions
CELER_FUNCTION	RejectionSampler (real_type f, real_type fmax)
	Construct with acceptance probability and maximum probability.
CELER_FUNCTION	RejectionSampler (real_type f)
	Construct when the distribution's maximum is normalized.
template<class Generator >
CELER_FUNCTION result_type	operator() (Generator &rng)
template<class Generator >
CELER_FUNCTION auto	operator() (Generator &rng) -> result_type
	Sample a random number according to the distribution.

Detailed Description

template<class RealType = ::celeritas::real_type>
class celeritas::RejectionSampler< RealType >

Return whether a rejection loop needs to continue trying.

A common implementation of sampling from a "difficult" (non-analytically invertible) probability distribution function is to bound the difficult distribution f(x) with another easily sampled function g(x) . Given a maximum value M over the x interval being sampled, it is equivalent to sampling f(x) by instead sampling from g(x) and rejecting with probability

\[ \frac{f(x)}{M g(x)} \]

These invocations generate statistically equivalent results:

• BernoulliDistribution(1 - p / pmax)(rng);
• !BernoulliDistribution(p / pmax)(rng);
• p < pmax * UniformDistribution{}(rng)
• RejectionSampler(p, pmax)(rng);

This is meant for rejection sampling, e.g., on cross section:

do {
  xs = sample_xs(rng);
} while (RejectionSampler{xs, xs_max}(rng));

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The documentation for this class was generated from the following file:

• RejectionSampler.hh