Module functions 

Auto-generated module

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Traits

QuasiRandomSequence

Trait for quasi-random sequences.

Functions

blue_noise_2d

Generate 2D Poisson disk (blue noise) samples using Bridson’s algorithm.

bootstrap_mean_ci

Bootstrap estimate of the mean and its 95% confidence interval.

bootstrap_resample

Bootstrap resample data with replacement, returning n resampled datasets.

effective_sample_size

Effective sample size (ESS) from normalized importance weights.

halton_multivariate

Generate n multi-dimensional Halton points using the first n_dims primes as bases.

halton_sequence

Van der Corput / Halton sequence in a given base. Returns n values in [0,1).

importance_sample

Discrete inverse-CDF (importance) sampling.

importance_sampling_estimate

Compute IS estimate of E\[h(X)\] where X ~ target but samples are drawn from proposal.

importance_weights

Compute unnormalized importance weights for a set of samples.

latin_hypercube_sample

Latin Hypercube Sample: n points in d dimensions, each in [0,1).

lhs_maximin

Latin Hypercube Sampling with maximin distance optimization.

mc_antithetic

Antithetic variates Monte Carlo estimator.

mc_control_variate

Control-variate Monte Carlo estimator.

monte_carlo_integrate

Monte Carlo integration of f over \[a, b\] using n uniform samples.

monte_carlo_integrate_nd

Monte Carlo integration of f over the unit hypercube \[0,1)^d in d dimensions.

qmc_integrate_halton

Quasi-Monte Carlo integration using the Halton sequence.

qmc_integrate_sobol

Quasi-Monte Carlo integration using the 1-D Sobol sequence.

rejection_sample_1d

Draw n samples from an arbitrary 1-D PDF using rejection sampling.

rejection_sample_2d

2-D rejection sampling.

sample_gaussian_3d

Sample a 3-D Gaussian vector with zero mean and isotropic standard deviation sigma.

sample_maxwell_boltzmann

Sample a 3-D velocity vector from the Maxwell–Boltzmann distribution.

sample_sphere_surface

Sample a point uniformly on the surface of the unit sphere (Marsaglia 1972).

sample_sphere_volume

Sample a point uniformly inside the closed unit ball (rejection sampling).

self_normalized_is

Self-normalized importance sampling estimator for E_target\[h(X)\].

sobol_sequence

Sobol’ quasi-random sequence: n points in d dimensions (up to 3 dims).

sobol_sequence_10d

Generate n Sobol points in 10 dimensions.

stratified_sample_1d

Stratified 1-D sampling.

stratified_sample_1d_rng

Stratified (jittered) 1-D samples using rand::Rng.

stratified_sample_nd

Generate n_samples stratified samples in n_dims dimensions.

stratified_unit_hypercube

Generate n_samples stratified samples over the unit hypercube \[0,1)^d.

systematic_resample

Systematic resampling for particle filters.