Constructor and Description |
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BoltzAnnealingFunction(RandomVectorGenerator rvg)
Constructs a new instance that uses a given RVG.
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FastAnnealingFunction(RandomVectorGenerator rvg)
Constructs a new instance that uses a given RVG.
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SimpleAnnealingFunction(RandomVectorGenerator rvg) |
Modifier and Type | Interface and Description |
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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 | Method and Description |
---|---|
static RandomVectorGenerator |
RNGUtils.synchronizedRVG(RandomVectorGenerator rng)
Returns a synchronized (thread-safe)
RandomVectorGenerator
backed by a specified generator. |
Modifier and Type | Method and Description |
---|---|
static List<double[]> |
RNGUtils.nextN(RandomVectorGenerator rvg,
int n)
Generates
n random vectors from a given random vector generator. |
static RandomVectorGenerator |
RNGUtils.synchronizedRVG(RandomVectorGenerator rng)
Returns a synchronized (thread-safe)
RandomVectorGenerator
backed by a specified generator. |
Modifier and Type | Class and Description |
---|---|
class |
ConcurrentCachedRVG
This is a fast thread-safe wrapper for random vector generators.
|
Constructor and Description |
---|
ConcurrentCachedRVG(RandomVectorGenerator rvg)
Constructs a new instance which wraps the given random vector generator
and uses a cache which has 8 entries per available core.
|
ConcurrentCachedRVG(RandomVectorGenerator rvg,
int cacheSize)
Constructs a new instance which wraps the given random vector generator
and uses a cache of the specified size.
|
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.
|
Constructor and Description |
---|
BurnInRVG(RandomVectorGenerator rvg,
int burnInSamples)
Construct a burn-in RVG.
|
ThinRVG(RandomVectorGenerator rvg,
int m)
Constructs a thinned RVG.
|
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 | Class and Description |
---|---|
class |
MultivariateRandomProcess
This interface represents a multivariate random process a.k.a.
|
class |
MultivariateRandomWalk
This is the Random Walk construction of a multivariate stochastic process per SDE specification.
|
Modifier and Type | Class and Description |
---|---|
class |
VARIMASim
This class simulates a multivariate ARIMA process.
|
Constructor and Description |
---|
VARIMASim(VARIMAModel arima,
RandomVectorGenerator rvg)
Construct a multivariate ARIMA model.
|
VARIMASim(VARIMAModel arima,
Vector[] lags,
Vector[] innovations,
RandomVectorGenerator rvg)
Construct a multivariate ARIMA model.
|
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