Modifier and Type | Class and Description |
---|---|
class |
ProbabilityMassSampler<X>
A random sampler that is constructed ad-hoc from a list of values and their probabilities.
|
Modifier and Type | Interface and Description |
---|---|
interface |
BivariateEVD
Bivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of
two-dimensional iid random vectors.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractBivariateEVD |
class |
BivariateEVDAsymmetricLogistic
The bivariate asymmetric logistic model.
|
class |
BivariateEVDAsymmetricMixed
The asymmetric mixed model.
|
class |
BivariateEVDAsymmetricNegativeLogistic
The bivariate asymmetric negative logistic model.
|
class |
BivariateEVDBilogistic
The bilogistic model.
|
class |
BivariateEVDColesTawn
The Coles-Tawn model.
|
class |
BivariateEVDHuslerReiss
The Husler-Reiss model.
|
class |
BivariateEVDLogistic
The bivariate logistic model.
|
class |
BivariateEVDNegativeBilogistic
The negative bilogistic model.
|
class |
BivariateEVDNegativeLogistic
The bivariate negative logistic model.
|
Modifier and Type | Class and Description |
---|---|
class |
InverseTransformSamplingEVDRNG
Generate random numbers according to a given univariate extreme value distribution, by
inverse transform sampling.
|
Modifier and Type | Class and Description |
---|---|
class |
ExtremeValueMC
Simulation of first order extreme value Markov chains such that each pair of consecutive values
has the dependence structure of given bivariate extreme value distributions.
|
Modifier and Type | Class and Description |
---|---|
class |
MARMASim
Generate random numbers based on a given MARMA model.
|
Modifier and Type | Class and Description |
---|---|
class |
HiddenMarkovModel |
class |
HMMRNG
In a (discrete) hidden Markov model, the state is not directly visible, but
output, dependent on the state, is visible.
|
Modifier and Type | Class and Description |
---|---|
class |
BaumWelch
This implementation trains an HMM model by observations using the Baum–Welch
algorithm.
|
class |
DiscreteHMM
This is the discrete hidden Markov model as defined by Rabiner.
|
Modifier and Type | Class and Description |
---|---|
class |
MixtureHMM
This is the mixture hidden Markov model (HMM).
|
class |
MixtureHMMEM
The EM algorithm is used to find the unknown parameters of a hidden Markov
model (HMM) by making use of the forward-backward algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
SimpleMC
This is a time-homogeneous Markov chain with a finite state space.
|
Modifier and Type | Class and Description |
---|---|
class |
ConstantSeeder<T extends Seedable>
A wrapper that seeds each given seedable random number generator with the given seed(s).
|
Modifier and Type | Interface and Description |
---|---|
static interface |
ConcurrentCachedGenerator.Generator<T>
Defines a generic generator of type
T . |
Modifier and Type | Class and Description |
---|---|
class |
ConcurrentCachedGenerator<T>
A generic wrapper that makes an underlying item generator thread-safe by
caching generated items in a concurrently-accessible list.
|
class |
ConcurrentCachedRLG
This is a fast thread-safe wrapper for random long generators.
|
class |
ConcurrentCachedRNG
This is a fast thread-safe wrapper for random number generators.
|
class |
ConcurrentCachedRVG
This is a fast thread-safe wrapper for random vector generators.
|
Modifier and Type | Class and Description |
---|---|
class |
ContextRNG<T>
This uniform number generator generates independent sequences of random numbers per context.
|
class |
ThreadIDRLG
This uniform number generator generates independent sequences of random numbers per thread, hence
thread-safe.
|
class |
ThreadIDRNG
This random number generator generates independent sequences of random numbers per thread, hence
thread-safe.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomVectorGenerator
A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
BurnInRVG
A burn-in random number generator discards the first M samples.
|
class |
HypersphereRVG
Generates uniformly distributed points on a unit hypersphere.
|
class |
IID
An i.i.d.
|
class |
MultinomialRVG
A multinomial distribution puts N objects into K bins according to the bins'
probabilities.
|
class |
NormalRVG
A multivariate Normal random vector is said to be p-variate normally distributed if every linear
combination of its p components has a univariate normal distribution.
|
class |
ThinRVG
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
|
class |
UniformDistributionOverBox
This random vector generator uniformly samples points over a box region.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractHybridMCMC
Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional
Metropolis algorithm, with molecular dynamics simulation.
|
class |
ErgodicHybridMCMC
A simple decorator which will randomly vary dt between each sample.
|
class |
HybridMCMC
This class implements a hybrid MCMC algorithm.
|
class |
MultipointHybridMCMC
A multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the
proposal generation instead of considering only the last configuration after the dynamics
simulation, we pick a proposal from a window of the last M configurations.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMetropolis
The Metropolis algorithm is a Markov Chain Monte Carlo algorithm, which requires only a function
f proportional to the PDF from which we wish to sample.
|
class |
Metropolis
This basic Metropolis implementation assumes using symmetric proposal function.
|
class |
MetropolisHastings
A generalization of the Metropolis algorithm, which allows asymmetric proposal
functions.
|
class |
RobustAdaptiveMetropolis
A variation of Metropolis, that uses the estimated covariance of the target
distribution in the proposal distribution, based on a paper by Vihola (2011).
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomLongGenerator
A (pseudo) random number generator that generates a sequence of
long s that lack any pattern and are uniformly distributed. |
interface |
RandomNumberGenerator
A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.
|
Modifier and Type | Class and Description |
---|---|
class |
BernoulliTrial
A Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes,
"success" and "failure", in which the probability of success, p, is the same every time
the experiment is conducted.
|
class |
BinomialRNG
This random number generator samples from the binomial distribution.
|
class |
BurnInRNG
A burn-in random number generator discards the first M samples.
|
class |
InverseTransformSampling
Inverse transform sampling (also known as inversion sampling, the inverse probability integral
transform, the inverse transformation method, Smirnov transform, golden rule, etc.)
is a basic method for pseudo-random number sampling,
i.e.
|
class |
LogNormalRNG
This random number generator samples from the log-normal distribution.
|
class |
RayleighRNG
This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.
|
class |
ThinRNG
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
|
class |
WeibullRNG
This random number generator samples from the Weibull distribution using the inverse transform sampling method.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomBetaGenerator
This is a random number generator that generates random deviates according to the Beta distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
Cheng1978
Cheng, 1978, is a new rejection method for generating beta variates.
|
class |
VanDerWaerden1969
Deprecated.
Cheng1978 is a much better algorithm. |
Modifier and Type | Interface and Description |
---|---|
interface |
RandomExpGenerator
This is a random number generator that generates random deviates according to the exponential distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
InverseTransformSamplingExpRNG
This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.
|
class |
Ziggurat2000Exp
This implements the ziggurat algorithm to sample from the exponential distribution.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomGammaGenerator
This is a random number generator that generates random deviates according to the Gamma distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
InverseTransformSamplingGammaRNG
Deprecated.
There exist much more efficient algorithms.
|
class |
KunduGupta2007
Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized
exponential distribution,
when the shape parameter lies between 0 and 1.
|
class |
MarsagliaTsang2000
Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled
normal variate.
|
class |
XiTanLiu2010a
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on
the ratio-of-uniforms method and logarithmic transformations of gamma random variable.
|
class |
XiTanLiu2010b
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on
the ratio-of-uniforms method and logarithmic transformations of gamma random variable.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomStandardNormalGenerator
This is a random number generator that generates random deviates according to the standard Normal
distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
BoxMuller
The Box-Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958)
is a pseudo-random number sampling method for generating pairs of independent standard
normally distributed
(zero expectation, unit variance) random numbers,
given a source of uniformly distributed random numbers.
|
class |
ConcurrentStandardNormalRNG |
class |
MarsagliaBray1964
The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method
for generating a pair of independent standard normal random variables.
|
class |
NormalRNG
This is a random number generator that generates random deviates according to the Normal
distribution.
|
class |
StandardNormalRNG
An alias for
Zignor2005 to provide a default implementation for sampling from the
standard Normal distribution. |
class |
Ziggurat2000
The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.
|
class |
Zignor2005
This is an improved version of the Ziggurat algorithm as proposed in the reference.
|
Modifier and Type | Class and Description |
---|---|
class |
InverseTransformSamplingTruncatedNormalRNG
A random variate x defined as
\[
x = \Phi^{-1}( \Phi(\alpha) + U\cdot(\Phi(\beta)-\Phi(\alpha)))\sigma + \mu
\]
with \(\Phi\) the cumulative distribution function and \(\Phi^{-1}\) its inverse, U a
uniform random number on (0, 1), follows the distribution truncated to the range (a,
b).
|
Modifier and Type | Class and Description |
---|---|
class |
Knuth1969
This is a random number generator that generates random deviates according to the Poisson
distribution.
|
Modifier and Type | Class and Description |
---|---|
class |
MWC8222
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.
|
class |
SHR0
SHR0 is a simple uniform random number generator.
|
class |
SHR3
SHR3 is a 3-shift-register generator with period 2^32-1.
|
class |
UniformRNG
A pseudo uniform random number generator samples numbers from the unit interval, [0, 1],
in such a way that there are equal probabilities of them falling in any same length sub-interval.
|
Modifier and Type | Interface and Description |
---|---|
interface |
LinearCongruentialGenerator
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers
based on a linear recurrence relation.
|
Modifier and Type | Class and Description |
---|---|
class |
CompositeLinearCongruentialGenerator
A composite generator combines a number of simple
LinearCongruentialGenerator , such as Lehmer ,
to form one longer period generator by first summing values and then taking modulus. |
class |
LEcuyer
This is the uniform random number generator recommended by L'Ecuyer in 1996.
|
class |
Lehmer
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in
[0, 1].
|
class |
MRG
A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form:
|
Modifier and Type | Class and Description |
---|---|
class |
MersenneTwister
Mersenne Twister is one of the best pseudo random number generators
available.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ObjectResampler<X>
This is the interface of a re-sampler method for objects.
|
interface |
Resampler
This is the interface of a re-sampler method.
|
Modifier and Type | Class and Description |
---|---|
class |
CaseResamplingReplacement
This is the classical bootstrap method described in the reference.
|
class |
CaseResamplingReplacementForObject<X>
This is the classical bootstrap method described in the reference.
|
Modifier and Type | Class and Description |
---|---|
class |
PattonPolitisWhite2009
This class implements the stationary and circular block bootstrapping method
with optimized block length.
|
class |
PattonPolitisWhite2009ForObject<X>
This class implements the stationary and circular block bootstrapping method
with optimized block length.
|
Modifier and Type | Interface and Description |
---|---|
interface |
MultivariateResampler
This is the interface of a multivariate re-sampler method.
|
Modifier and Type | Class and Description |
---|---|
class |
GroupResampler |
Modifier and Type | Interface and Description |
---|---|
interface |
MultivariateRandomRealizationGenerator
This interface defines a generator to construct random realizations from a multivariate stochastic process.
|
Modifier and Type | Class and Description |
---|---|
class |
MultivariateBrownianRRG
This is the Random Walk construction of a multivariate Brownian motion.
|
class |
MultivariateRandomProcess
This interface represents a multivariate random process a.k.a.
|
class |
MultivariateRandomRealizationOfRandomProcess
This class generates random realizations from a multivariate random/stochastic process.
|
class |
MultivariateRandomWalk
This is the Random Walk construction of a multivariate stochastic process per SDE specification.
|
Modifier and Type | Interface and Description |
---|---|
interface |
RandomRealizationGenerator
This interface defines a generator to construct random realizations from a univariate stochastic process.
|
Modifier and Type | Class and Description |
---|---|
class |
RandomProcess
This interface represents a univariate random process a.k.a.
|
class |
RandomRealizationOfRandomProcess
This class generates random realizations from a random/stochastic process.
|
class |
RandomWalk
This is the Random Walk construction of a stochastic process per SDE
specification.
|
Modifier and Type | Class and Description |
---|---|
class |
OUSim
This class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process.
|
Modifier and Type | Class and Description |
---|---|
class |
AS159
Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the
lists of row and column sums.
|
Modifier and Type | Class and Description |
---|---|
class |
VARIMASim
This class simulates a multivariate ARIMA process.
|
Modifier and Type | Class and Description |
---|---|
class |
ARIMASim
This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.
|
Modifier and Type | Class and Description |
---|---|
class |
GARCHSim
This class simulates the GARCH models of this form.
|
Modifier and Type | Class and Description |
---|---|
class |
GroupResamplerFactory
Creates re-samplers that do re-sampling for the whole group of stocks
together.
|
Modifier and Type | Class and Description |
---|---|
class |
KnightSatchellTran1995
Implements the Knight-Satchell-Tran model of financial asset returns.
|
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