all rv samplers must inherit from this. More...

#include <chrono>
#include <Eigen/Dense>
#include <random>

Include dependency graph for rv_samp.h:

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Classes
class	pf::rvsamp::rvsamp_base
	Base class for all random variable sampler types. Primary benefit is that it sets the seed for you. More...

class	pf::rvsamp::UnivNormSampler< float_t >
	A class that performs sampling from a univariate Normal distribution. More...

class	pf::rvsamp::UnivStudTSampler< float_t >
	A class that performs sampling from Student's T distribution. More...

class	pf::rvsamp::UnivLogNormSampler< float_t >
	A class that performs sampling from a univariate Log-Normal distribution. More...

class	pf::rvsamp::UnivGammaSampler< float_t >
	A class that performs sampling from a univariate Gamma distribution. More...

class	pf::rvsamp::UnivInvGammaSampler< float_t >
	A class that performs sampling from a univariate Inverse Gamma distribution. More...

class	pf::rvsamp::TruncUnivNormSampler< float_t >
	A class that performs sampling from a truncated univariate Normal distribution. More...

class	pf::rvsamp::PoissonSampler< float_t, int_t >
	A class that performs sampling from a Poisson distribution. More...

class	pf::rvsamp::BernSampler< float_t, int_t >
	A class that performs sampling from a univariate Bernoulli distribution. More...

class	pf::rvsamp::MVNSampler< dim, float_t >
	A class that performs sampling from a multivariate normal distribution. More...

class	pf::rvsamp::UniformSampler< float_t >
	A class that performs sampling from a continuous uniform distribution. More...

class	pf::rvsamp::k_gen< N, float_t >
	A class that performs sampling with replacement (useful for the index sampler in an APF) More...

class	pf::rvsamp::BetaSampler< float_t >
	A class that performs sampling from a Beta distribution. More...

Detailed Description

all rv samplers must inherit from this.

Samples from Beta distribution.

Basically a wrapper for std::discrete_distribution<> outputs are in the rage (0,1,...N-1)

Can sample from a distribution with fixed mean and covariance, fixed mean only, fixed covariance only, or nothing fixed.

Samples from univariate Bernoulli distribution.

Samples from univariate Poisson distribution.

Samples from a truncated univariate Normal distribution using the acceptance rejection method. The proposal distribution used is a normal distribution with the same location and scale parameters as the target. As a result, this method will take a long time when the width of the support of the target is narrow.

Samples from univariate Inverse Gamma distribution.

Samples from univariate Gamma distribution.

Samples from univariate Log-Normal distribution.

Samples from from Student's T distribution.

Samples from univariate Normal distribution.

Template Parameters

dim	the dimension of each random vector sample.

Classes

Detailed Description