mc::estimators::VolumeEstimatorMC< dim > Class Template Reference

Hit-or-Miss Monte Carlo volume estimator. More...

#include <VolumeEstimatorMC.hpp>

Collaboration diagram for mc::estimators::VolumeEstimatorMC< dim >:

Public Member Functions
VolumeEstimate< dim >	estimate (const mc::domains::IntegrationDomain< dim > &domain, std::uint32_t seed, std::size_t n_samples) const
	Estimate the volume of a domain using hit-or-miss sampling.

Detailed Description

template<std::size_t dim>
class mc::estimators::VolumeEstimatorMC< dim >

Hit-or-Miss Monte Carlo volume estimator.

Template Parameters

dim	Dimensionality of the space.

Estimates the volume of an arbitrary domain Ω by sampling uniformly in the bounding box and counting the fraction of samples inside Ω.

Algorithm:

1. Sample N points uniformly from bounding box B
2. Count hits: N_hits = |{x ∈ B : x ∈ Ω}|
3. Estimate: V̂_Ω = V_B · (N_hits / N)
4. Compute standard error from Bernoulli variance

Note

This estimator is unbiased but can be slow for domains with small volume.

Used internally by integrators for two-stage integration (e.g., MH-based).

Definition at line 68 of file VolumeEstimatorMC.hpp.

Member Function Documentation

estimate()

template<std::size_t dim>

VolumeEstimate< dim > mc::estimators::VolumeEstimatorMC< dim >::estimate	(	const mc::domains::IntegrationDomain< dim > &	domain,
		std::uint32_t	seed,
		std::size_t	n_samples
	)		const

Estimate the volume of a domain using hit-or-miss sampling.

Parameters

domain	The integration domain whose volume to estimate.
seed	Random seed for reproducibility.
n_samples	Number of sample points to evaluate.

Returns

VolumeEstimate containing volume, stderr, hit ratio, and sample count.

Exceptions

std::invalid_argument

if n_samples == 0.

Uses OpenMP parallelization for efficient parallel sampling. Each thread gets a deterministic RNG stream based on seed and thread ID.

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

• src/montecarlo/estimators/VolumeEstimatorMC.hpp