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SSJ V. labo. |
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StatProbe,
for collecting statistics on a
variable that evolves in simulation time, with a piecewise-constant trajectory.activeTests, formatActiveTests, etc.
tests, then computes the p-values of those
that currently belong to activeTests,
and return these quantities in sVal and pVal, respectively.
add
method that can be used when there is only one output variable.
add
that can be used when p = q = 1.
add (x.toArray()),
without copying the elements of x into a temporary 2D array.
add (x.toArray()),
without copying the elements of x
into a temporary 3D array.
rows -
1, and c = 0, ..., columns - 1.
add for a 2D array.
add, but adds the new event ev
immediately after the event other in the list.
add, but adds the new event ev
immediately before the event other in the list.
addSameDimension x.toArray(),
without copying the elements of x into a temporary 1D array.
andersonDarling).
AndersonDarlingDist for the
distribution (see).RandomStream to
return antithetic variates.s static method to append str to the buffer.
f static method to append x to the buffer.
f static method to append x to the buffer.
d static method to append x to the buffer.
d static method to append x to the buffer.
format
static method with the same four arguments to append x to the buffer.
MarkovChainComparable
using the array-RQMC method of[#!vLEC05a!#], where n copies of
the chain are simulated in parallel, and sorted using a
multi-dimensional sort (see
MultiDimSort) at each
step of the chain.ArrayOfComparableChains(baseChain, new RandomShift(new MRG32k3a()), new SplitSort(baseChain.stateDim)).
ArrayOfComparableChains, except that instead of
working with n clones of a MarkovChain, we use a single
MarkovChainDouble object for all the chains.ArrayOfDoubleChains (baseChain, new RandomShift(new MRG32k3a())).
isDouble.
isFloat.
isInt.
average, and stores
the results into the array a.
average,
and stores it into the given matrix m.
RandomStream.barF (alpha, beta, 0, 1, x).
barF (alpha, 1.0, d, x).
NormalDist.barF01.
barF (0, 1, x).
barF (0.0, 1.0, x).
BernoulliDist).ContinuousDistribution for
the beta distribution with shape parameters
α > 0 and β > 0, over the interval [a, b], where a < b.BetaRejectionLoglogisticGen (s, s, dist).
BetaDist to the case of a symmetrical
beta distribution over the interval [0, 1],
with shape parameters
α = β.DiscShiftBaker1
[see eq.], but uses multi-precision real numbers.DiscShiftBaker1Lattice
[see eq.], but uses multi-precision real numbers.EventList using a binary search tree.DiscreteDistributionInt for the
binomial distribution with parameters n and p, where
n is a positive integer and
0 <= p <= 1.ContinuousDistribution2Dim for the bivariate
normal distribution.BiNormalDist for the bivariate
normal distribution
using a translation of Donnelly's FORTRAN code.BiNormalDonnellyDist (rho, 15).
BiNormalDonnellyDist (mu1, sigma1, mu2, sigma2, rho, 15).
BiNormalDist for the bivariate
normal distribution
using Genz's algorithm as described in.ContinuousDistribution2Dim for the standard bivariate Student's t distribution.CategoryChart.close.cdf (alpha, beta, 0, 1, x).
cdf (alpha, alpha, d, x).
NormalDist.cdf01.
cdf (0, 1, x).
cdf (0.0, 1.0, x).
chi2,
except that the expected
number of observations per category is assumed to be the same for
all categories, and equal to nbExp.
ContinuousDistribution for the chi
distribution with shape parameter
v > 0, where the number of degrees of freedom
v is a positive integer.ChiSquareDist with
faster but less accurate methods.ContinuousDistribution for
the noncentral chi-square distribution with ν degrees of freedom
and noncentrality parameter λ, where ν > 0 and
λ > 0.CloneableRandomStream extends RandomStream and Cloneable.computeDensity ,
but the bandwidth h is obtained from the method
KernelDensityGen.getBaseBandwidth(dist) in package randvar.
connectToDatabase (url.openStream()).
connectToDatabase (new FileInputStream (file)).
connectToDatabase (new FileInputStream (fileName)).
connectToDatabase with the stream obtained from
the resource resource.
covariance for computing
the sample correlation matrix.
FunctionOfMultipleMeansTally.
FunctionOfMultipleMeansTally.
Event object used for synchronization.
Tally, q being the number of control variables.
Tally.
Tally.
Tally.
TallyStore, q being the number of control variables.
TallyStore.
TallyStore.
TallyStore.
HistogramType.FREQUENCY.
CycleBasedPointSet, except that the successive
values in the cycles are stored as integers in the range
{0,..., 2k -1}, where
1 <= k <= 31.d (0, 1, x).
d (fieldwidth, 1, x).
DataReader.Sim, and the no-argument constructor of Event.
density (alpha, beta, 0, 1, x).
density (0, 1, x).
diff(IntArrayList,IntArrayList,int,int,int,int),
but for the continuous case.
DigitalNetBase2FromFile(filename, r, 31, s1) where
s1 is the dimension and r is given in data file filename.
compareTo can be called for this object.
ContinuousDistributionMulti for the
Dirichlet distribution with parameters
(α1,...,αd),
αi > 0.RandomMultivariateGen for a
Dirichlet distribution.DiscrepancyContainer for the given
discrepancies.
DiscreteDistribution(double[], double[], int).
EventList using a doubly linked linear list.String.
String.
E (0, 6, x).
E (fieldwidth, 6, x).
e (0, 6, x).
e (fieldwidth, 6, x).
DiscreteDistribution to an empirical
distribution function,
based on the observations
X(1),..., X(n) (sorted by increasing order).formatp0 to determine
which p-values are too close to 0 or 1 to be printed explicitly.
evaluate(double) -
Method in class umontreal.iro.lecuyer.functionfit.BSpline
actions method.
exhaust(s), except that only
values of aj relatively prime to n are considered.
exhaust(s), except that only
values of aj relatively prime to n are considered.
ContinuousDistribution for
the exponential distribution
with mean 1/λ where
λ > 0.ExponentialDist class with a constructor accepting as
argument the mean 1/λ instead of the rate λ.f (0, 6, x).
f (fieldwidth, 6, x).
RandomStream interface by using as a backbone
generator the combination of the WELL607 proposed in
(and implemented in WELL607) with a nonlinear generator.FaureSequence(b, k, w, w, dim)
with base b equal to the smallest prime larger or equal to dim,
and with at least n points.
FDist, except that it provides static methods
to compute or approximate the complementary distribution function of X,
which we define as
bar(F)(x) = P[X >= x], instead of
F(x) = P[X <= x].ContinuousDistribution for
the folded normal distribution with
parameters μ >= 0 and
σ > 0.d (0, 1, x).
String with a minimum length
of fieldwidth, the result is right-padded with spaces if
necessary but it is not truncated.
String containing x.
format, except it formats the given
value for the locale locale.
formatBase (0, b, x).
String representation in base
b.
confidenceIntervalDelta,
but returns the confidence interval in a formatted string of the form
``95% confidence interval for function of means: (32.431, 32.487)'',
using d decimal digits of accuracy.
formatCIDelta (level, 3).
confidenceIntervalNormal.
confidenceIntervalStudent.
confidenceIntervalVarianceChi2.
formatKS,
but for the KS statistic DN+(a).
formatKS,
but for DN+(a).
formatp0 to print p, and adds
the marker ``****'' if p is considered suspect
(uses the environment variable SUSPECTP for this).
formatp1.
formatWithError,
except that it formats the given value and error for the
locale locale.
formatWithError,
except that it formats the given value and error for the
locale locale.
ContinuousDistribution for the Fréchet
distribution, with location parameter δ, scale
parameter β > 0, and shape parameter
α > 0, where we use
the notation
z = (x - δ)/β.G (0, 6, x).
G (fieldwidth, 6, x).
g (0, 6, x).
g (fieldwidth, 6, x).
ContinuousDistribution for
the gamma distribution with
shape parameter
α > 0 and scale parameter
λ > 0.GammaProcessPCA, but the generated uniforms
correspond to a bridge transformation of the BrownianMotionPCA
instead of a sequential transformation.GammaProcessPCABridge, but uses the fast inversion method
for the symmetrical beta distribution, proposed by L'Ecuyer and Simard, to accelerate the generation of the beta random variables.gaussLobatto(MathFunction, double, double, double), but
also returns in T[0] the subintervals of integration, and in
T[1], the partial values of the integral over the corresponding
subintervals.
RandomStream's.
RandomStream interface via inheritance
from RandomStreamBase.BrownianMotion.
DiscreteDistributionInt for
the geometric distribution with parameter
p, where 0 < p < 1.BrownianMotionPCA.
BrownianMotion object
used to generate the process.
MultivariateBrownianMotion object
used to generate the process.
BrownianMotion.
BrownianMotionPCA.
StringBuffer associated with that object.
MarkovChainComparable.
ContinuousDistribution
object by executing the code contained in the string str.
Sim.
getContinuousDistribution, but for discrete distributions
over the real numbers.
getContinuousDistribution, but for discrete distributions
over the integers.
Distribution used by this generator.
DoubleArrayList
object that contains the observations for this probe.
getField,
except that it can return non-public fields.
Polynomial instance used to evaluate
x, in an ArrayList table instance returned by
getSplinePolynomials().
GammaProcess.
NormalGen used.
GammaProcess object gneg
used to generate the
Γ- component of the process.
GammaProcess object gpos
used to generate the
Γ+ component of the process.
initStat was called.
update method (or the initial value if
update was never called after init).
getMethod, except that it can return non-public methods.
Discrepancy.
getLambda.
PointSetRandomization.
getRequiredNewObservations
with the average, confidence interval radius, and
number of observations
given by the statistical probe probe.
getRequiredNewObservations with
the average, confidence interval radius, and number of observations given by
the tally ta.
getRequiredNewObservations with
the average, confidence interval radius, and number of observations given by
the function of multiple means fmmt.
MultiDimSort used.
RandomStream used by this generator.
RandomStream used by this object.
RandomStream
for the underlying Brownian motion.
RandomStream's are the same.
InverseGaussianProcess.
BrownianMotion process, which should
be the same as for the GammaProcess.
evaluate in the
case where the 0/0 function is calculated.
AbstractChrono class to compute the global CPU time used
by the Java Virtual Machine.ContinuousDistribution for the half-normal
distribution with parameters μ and
σ > 0.resetStartProcess.
EventList using the doubly-linked
indexed list of Henriksen (see also).BatchSort, except that when applying the batch sort,
the objects are also given labels that map them to the d-dimensional unit hypercube
[0, 1)d as explained below,
and then re-ordered by following a Hilbert curve as in the HilbertCurveSort.HilbertCurveBatchSort that will use batches.
HilbertCurveBatchSort that will use
batchesExponents.
TallyStore object.
TallyHistogram object.
ContinuousDistribution for
the hyperbolic secant distribution with location
parameter μ and scale parameter
σ > 0.DiscreteDistributionInt for
the hypergeometric distribution with
k elements chosen among l, m being
of one type, and l - m of the other.RandomMultivariateGen for a vector of independent identically distributed
(i.i.d.) random variables.init(n,"","Parameter", "Discrepancy").
init followed by update(x).
init, but also chooses evlist as the
event list to be used.
init, but also chooses evlist as the
event list to be used.
SplayTree algorithm
as EventList.
EventList.
init on each element.
initialState() on each chain.
setStatCollecting (true) and makes an update for the
probe on the list size.
Introspection - Class in umontreal.iro.lecuyer.utilinverseF (alpha, beta, 0, 1, u).
inverseF (alpha, 1, d, u).
NormalDist.inverseF01.
inverseF (0, 1, u).
inverseF (0.0, 1.0, u).
ContinuousDistribution for
the inverse gamma distribution with shape parameter
α > 0 and scale parameter β > 0.ContinuousDistribution for
the inverse Gaussian distribution with location parameter
μ > 0 and scale parameter
λ > 0.InverseGaussianDist.InverseGaussianProcess.iterator, except that the first coordinate
of the points is i/n, the second coordinate is obtained via
the generating matrix
C0, the next one via
C1,
and so on.
iterSpacingsTests, but with the
GofStat.powerRatios transformation.
GofStat.iterateSpacings
transformation to the
U(0),..., U(N-1), assuming that these observations are in
sortedData, then computes the EDF test statistics and calls
activeTests after each transformation.
ContinuousDistribution for
the Johnson SL distribution.JohnsonSLDist
(gamma, delta, 0, 1).
ContinuousDistribution for
the Johnson SU distribution.JohnsonSUDist
(gamma, delta, 0, 1).
kolmogorovSmirnov).
ContinuousDistribution for the
Kolmogorov-Smirnov distribution with parameter n.KolmogorovSmirnovDist for the distribution.FDist.kolmogorovSmirnovPlusJumpOne, assuming that F is the
uniform distribution over [0, 1] and that
U(1),..., U(n) are in sortedData.
KolmogorovSmirnovPlusDist
but for the case where the distribution function F has a jump of size
a at a given point x0, is zero at the left of x0,
and is continuous at the right of x0.
ContinuousDistribution for
the Laplace distribution.randomize
method will do a left matrix scramble followed by a random digital shift.
randomize
method will do a left matrix scramble followed by a random digital shift.
RandomStreamBase using a 64-bit composite linear feedback
shift register (LFSR) (or Tausworthe) RNG as defined in.ListWithStat, and
uses a linked list as the internal data structure.ListOfTallies to add support for the computation
of the sample covariance between each pair of elements
in a list, without storing all observations.ContinuousDistribution for the
logistic distribution.ContinuousDistribution for the
lognormal distribution.LognormalDist class with a constructor accepting the
mean m and the variance v of the distribution as arguments.derivative.integral method.matMatModM using double, but with int instead
of double.
matMatModM using double, but with long instead
of double.
matPowModM using double, but with int instead
of double.
matPowModM using double, but with long instead
of double.
matTwoPowModM using double, but with int instead of
double.
matTwoPowModM using double, but with long instead of
double.
matVecModM using double, but with int instead
of double.
matVecModM using double, but with long instead
of double.
RandomStreamBase by using as a
backbone (or main) generator the combined multiple recursive
generator (CMRG) MRG32k3a proposed by L'Ecuyer,
implemented in 64-bit floating-point arithmetic.MRG32k3a, except here it is implemented
with type long instead of double.RandomStream interface via inheritance from
RandomStreamBase.MultinormalGen for a multivariate normal distribution, generated via a Cholesky decomposition of the covariance
matrix.MultinormalCholeskyGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).
ContinuousDistributionMulti for the
multinormal distribution with mean vector μ and covariance
matrix
Σ.RandomMultivariateGen for a
multivariate normal (or multinormal) distribution.MultinormalGen for a multivariate normal distribution, generated via the method of principal components analysis
(PCA) of the covariance matrix.MultinormalPCAGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).
BitVector by a BitMatrix
and returns the result.
BitVector, by
a BitMatrix.
GeometricBrownianMotion.MultivariateBrownianMotion.
ClassFinder
when two or more fully qualified class names can be
associated with a simple class name.DiscreteDistributionIntMulti for the
negative multinomial distribution with parameters n > 0 and
(
p1,…, pd) such that all 0 < pi < 1 and
∑i=1dpi < 1.nextDouble (s, s, alpha, lambda).
nextDouble (in which the baker transformation is applied).
nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).
nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).
ContinuousDistribution for the normal
distribution (e.g.,).NormalDist (for the normal
distribution with mean μ and variance σ2).InverseGaussianProcess igP, constructs a
new NormalInverseGaussianProcess.
RandomStream's
are set to the same stream, streamAll.
Discrepancy and implements the methods required
to compute the Pα
figure of merit for a lattice point set Ψs
which is the intersection of a lattice L and the unit hypercube [0, 1)s
in s dimensions.ContinuousDistribution for a distribution
from the Pareto family, with
shape parameter
α > 0 and location parameter β > 0.InverseGammaDist.
InverseGammaGen.
DiscreteDistributionInt for the
Poisson distribution with mean
λ >= 0.PoissonGen).ContinuousDistribution for
the power distribution with shape parameter
c > 0, over the interval [a, b], where a < b.PPPlot
(title, XLabel, YLabel, dist, X)
above, except that only the first numPoints of X
are plotted.
String containing all the data of
the BitMatrix.
StringBuffer which defines new types
of append methods.RandomVariateGen.random(s, k), except that only
values of aj relatively prime to n are considered.
random(s, k), except that only
values of aj relatively prime to n are considered.
newInstance method
each time a new random stream is needed, instead of invoking
directly the specific constructor of the desired type.newInstance method.Rank1Lattice with n points and lattice
vector a of dimension s.
readCSVData to
obtain a matrix of strings from
the resource.
readDoubleData2D,
for reading strings.
readDoubleData2D,
for reading strings.
readDoubleData to
obtain an array of double-precision values from
the resource.
readDoubleData to
obtain an array of double-precision values from
the file.
readDoubleData to
obtain an array of double-precision values from
the file.
readDoubleData2D to
obtain a matrix of double-precision values from
the resource.
readDoubleData2D to
obtain a matrix of double-precision values from
the file.
readDoubleData2D to
obtain a matrix of double-precision values from
the file.
readDoubleData,
for reading integers.
readIntData to
obtain an array of integers from
the resource.
readDoubleData,
for reading integers.
readDoubleData,
for reading integers.
readDoubleData2D,
for reading integers.
readDoubleData to
obtain a matrix of integers from
the resource.
readDoubleData2D,
for reading integers.
readDoubleData2D,
for reading integers.
readStringData to
obtain an array of integers from
the resource.
readDoubleData,
for reading strings.
readDoubleData,
for reading strings.
EventList using a red black tree,
which is similar to a binary search tree except that
every node is colored red or black.regroupElements for an array list.
regroupElements for a 1D matrix.
regroupElements for a 2D matrix.
setStatCollecting (true) has been
called before for this list.
report), followed by a confidence interval
(as in formatCIDelta).
reportAndCIDelta
(level, 3).
report), followed by a confidence interval (as in
formatCIStudent), using d fractional decimal digits.
reportAndCIStudent (level, 3).
reset(i) for all indices i.
resetNextSubstream methods
of all streams in the list.
VarianceGammaProcess object used to generate this process.
BrownianMotion and the GammaProcess objects
used to generate this process.
GammaProcess objects used to generate this process.
resetStartStream methods
of all streams in the list.
resetStartSubstream methods
of all streams in the list.
Process objects.s (0, str).
scale(i,scale) for all indices i.
FBar.scan.
UserRecord objects
for the processes in the service list for this resource.
nextDouble.
nextDouble.
setRows, for setting the number of columns.
report, and shortReport.
report, and shortReport.
report, and shortReport.
report and shortReport.
report and shortReport.
XYChart.
XYChart.
\documentclass (and other) commands in the
created LATEX files.
setLambda
with argument 1/mean to change the mean of this distribution.
InverseGaussianProcessPCA
and the inner
BrownianMotionPCA.
MultivariateBrownianMotion.
InverseGaussianProcess.
GammaProcess.
GammaProcess'es.
PointSetRandomization
to rand.
MultiDimSort to
sort.
waitList for this bin.
waitList and servList for this resource.
RandomStream used by this generator to stream.
RandomStream used by this object to stream.
RandomStream
of the GammaGen and
the BetaGen to stream.
RandomStream
for the underlying Brownian motion to stream.
InverseGaussianProcess.
RandomStream's.
RandomStream
of the two GammaProcess'es to stream.
DatasetChangeEvent to all
registered listeners.
evaluate for
the undefined function 0/0 to zeroOverZero.
Simulator static methods.
PointSet p, where n is the current number of copies
(clones) of the chain and is assumed to equal the number of points
in p.
PointSet p,
where n is the current number of copies
(clones) of the chain and is assumed to equal the number of points
in p.
simulRQMC but also returns the results as a formatted string.
simulRuns but also returns the results as a formatted string.
simulRuns, except that the stream is first reset to
its initial seed and then reset to the first substream at the beginning
and to the next substream after each run.
simulRuns, except that the stream is first reset to
its initial seed and then reset to the first substream at the beginning
and to the next substream after each run.
simulRunsWithSubstreams but also returns the results
as a formatted string.
initialStateDouble, starts a new simulation run,
simulates numSteps steps of the
Markov chain using the given stream, and returns the final state.
MultiDimSort.
MultiDimSort.
EventList using a splay tree.MultiDimSort that performs a
split sort on the arrays.SplitSort that will use the first
d dimensions to sort.
variance.
UserRecord for this resource.
start.
start.
ContinuousDistribution for
the Student t-distribution
with n degrees of freedom, where n is a positive integer.PointSet object, initially identical to P,
and from which a subset of the points and/or a subset of the coordinates
is to be extracted.
sum, and stores
the results into the array s.
sum, and stores
it into the given matrix m.
formatp1 to determine
which p-values should be marked as suspect when printing test results.
AbstractChrono class to compute
the total system time using Java's builtin System.nanoTime.Tally which gives a more detailed view
of the observations statistics.Tally for which the individual
observations are stored in a list implemented as a
DoubleArrayList.simulRQMCFormat, but also gives the
variance improvement factor with respect to MC.
AbstractChrono
class to compute the CPU time for a single thread.Thread variable and initializes it to zero.
ThreadProcessSimulator variable.
PointSet set as an array
points[n][s].
XYChart.
toLatexCdf, but for the probability density instead
of the cdf.
String containing all the data of
the BitMatrix.
String.
getLongName.
DiscreteDistributionInt for
the discrete uniform distribution over the range [i, j].UnuranContinuous(s, s, genStr).
UnuranDiscreteInt (s, s, genStr).
UnuranEmpirical(s, s, dist, genStr).
UnuranEmpirical(s, aux, genStr), but reading
the observations from the empirical distribution dist.
update.
Resource or for Bin tokens,
or when a process waits for a Condition.valueOf (cls, name),
with case insensitive field name look-up.
GammaProcess
objects for
Γ+ and
Γ- are
set to those of and their
initial values
Γ+(t0) and
Γ-(t0) are set to t0.
VarianceGammaProcessDiff, but the two inner
GammaProcess'es are of PCA type.VarianceGammaProcessDiffPCA with
parameters
θ = VarianceGammaProcessDiffPCA with
parameters
θ = VarianceGammaProcessDiff, but the two
inner GammaProcess'es are of the type PCABridge.VarianceGammaProcessDiffPCABridge with
parameters
θ = viewCdf, but for the probability density instead
of the cdf.
UserRecord
for the processes waiting for tokens from this bin.
UserRecord
for the processes waiting for this condition.
UserRecord objects
for the processes in the waiting list for this resource.
warmup.
ContinuousDistribution for the
Watson G distribution (see).ContinuousDistribution for the
Watson U distribution (see).RandomStream interface via inheritance from
RandomStreamBase.RandomStream interface via inheritance
from RandomStreamBase.SSJXYSeriesCollection.XYLineChart.
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SSJ V. labo. |
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