Represents a function of multiple means tally for an estimator with linear control variables. More...

Inheritance diagram for FunctionOfMultipleMeansTallyWithCV:

Collaboration diagram for FunctionOfMultipleMeansTallyWithCV:

Public Member Functions
	FunctionOfMultipleMeansTallyWithCV (MultivariateFunction funcNoCV, int p, int q)
	Creates a new function of multiple means tally for a function `funcNoCV` of `p` variables, and with `q` control variables. More...

	FunctionOfMultipleMeansTallyWithCV (MultivariateFunction funcNoCV, ListOfTalliesWithCV< Tally > l)
	Constructs a new function of multiple means tally with control variables from the list of tallies `l`, and the function `funcNoCV`. More...

MultivariateFunction	getFunctionWithoutCV ()
	Returns the implementation computing the function \(g(\boldsymbol{\mu})\). More...

int	getNumControlVariables ()
	Returns the number of control variables being used. More...

int	getDimensionWithoutCV ()
	Returns the dimension of this tally excluding the control variables. More...

double	getBeta (int i)
	Returns the value of \(\beta_{\mathrm{f},i}\). More...

void	setBeta (int i, double b)
	Sets the value of \(\beta_{\mathrm{f},i}\). More...

double []	getBeta ()
	Returns the \(\boldsymbol{\beta}_{\mathrm{f}}\) vector. More...

void	setBeta (double[] beta)
	Sets the value of \(\boldsymbol{\beta}_{\mathrm{f}}\) vector to `beta`. More...

ListOfTalliesWithCV< Tally >	getListOfTalliesWithCV ()
	Returns the list of tallies with control variables used by this object.

double	getExpectedValue (int i)
	Gets the expected value of the \(i\)th component of \(\boldsymbol{\nu}\). More...

void	setExpectedValue (int i, double e)
	Sets the expected value of the \(i\)th component of \(\boldsymbol{\nu}\) to `e`. More...

double []	getExpectedValues ()
	Gets an array containing the vector \(\boldsymbol{\nu}\). More...

void	setExpectedValues (double[] exp)
	Sets the vector \(\boldsymbol{\nu}\) to to given array `exp`. More...

void	estimateBeta ()
	Uses the sample averages and covariances obtained from the internal list of tallies to estimate the \(\boldsymbol{\beta}_{\mathrm{f}}\) vector minimizing the variance of \(h(\bar{\mathbf{X}}_n, \mathbf{C})\). More...

void	estimateBetaFromMatrix (DoubleMatrix2D mbeta)
	Multiples the given \(q\times p\) matrix by the gradient \(\nabla g(\bar{\mathbf{X}}_n)\) to get an estimate of the \(\boldsymbol{\beta}_{\mathrm{f}}^*\) vector minimizing the variance. More...

FunctionOfMultipleMeansTallyWithCV	clone ()
	Clones this object and the function which is stored inside. More...

Public Member Functions inherited from FunctionOfMultipleMeansTally
	FunctionOfMultipleMeansTally (MultivariateFunction func, int d)
	Constructs a function of multiple means tally with dimension `d`, and function `func`. More...

	FunctionOfMultipleMeansTally (MultivariateFunction func, String name, int d)
	Constructs a function of multiple means tally with name `name`, dimension `d`, and function `func`. More...

	FunctionOfMultipleMeansTally (MultivariateFunction func, ListOfTalliesWithCovariance< Tally > ta)
	Constructs a function of multiple means tally using the function `func` and the list of tallies `ta` for observation management and covariance estimation. More...

void	setName (String name)

ListOfTalliesWithCovariance< Tally >	getListOfTallies ()
	Returns the (unmodifiable) list of tallies internally used by this object. More...

MultivariateFunction	getFunction ()
	Returns the function of multiple means used by this tally. More...

int	getDimension ()
	Returns the dimension of this tally, i.e., the size of any vector of observations. More...

void	add (double... x)
	). More...

int	numberObs ()
	Returns the number of vectors of observations given to this probe since its last initialization. More...

double	average ()
	Computes \(\bar{\nu}_n=g(\bar{\mathbf{X}}_n)\), an estimate of the function of means \(\nu\). More...

double	variance ()
	Estimates \(n\mathrm{Var}(g(\bar{\mathbf{X}}_n))\) where \(n\) is the number of vectors of observations given to this collector since the last initialization. More...

double	standardDeviation ()
	Returns the square root of variance. More...

void	confidenceIntervalDelta (double level, double[] centerAndRadius)
	Computes a confidence interval with confidence level `level` on \(\nu=g(\boldsymbol{\mu})\), using the delta theorem [214] . More...

String	formatCIDelta (double level, int d)
	Similar to #confidenceIntervalDelta(double,double[]), but returns the confidence interval in a formatted string of the form "<tt>95% confidence interval for function of means: (32.431, 32.487)</tt>", using \(d\) decimal digits of accuracy. More...

String	formatCIDelta (double level)
	Same as formatCIDelta(level, 3).

String	report ()
	Returns a string containing a formatted report on this probe. More...

String	reportAndCIDelta (double level, int d)
	Returns a string containing a formatted report on this probe (as in report ), followed by a confidence interval (as in formatCIDelta(double,int) ). More...

String	reportAndCIDelta (double level)
	Same as reportAndCIDelta(level, 3). More...

String	shortReportHeader ()

String	shortReport ()
	Formats and returns a short statistical report for this function of multiple means tally. More...

double	getConfidenceLevel ()
	Returns the level of confidence for the intervals on the mean displayed in reports. More...

void	setConfidenceLevel (double level)
	Sets the level of confidence for the intervals on the mean displayed in reports. More...

void	setConfidenceIntervalNone ()
	Indicates that no confidence interval needs to be printed in reports formatted by report, and shortReport. More...

void	setConfidenceIntervalDelta ()
	Indicates that a confidence interval on the true mean, based on the delta and central limit theorems, needs to be included in reports formatted by report, and shortReport. More...

void	setShowNumberObs (boolean showNumObs)
	Determines if the number of observations must be displayed in reports. More...

double	min ()
	Returns `Double.NaN`.

double	max ()
	Returns `Double.NaN`.

double	sum ()
	Returns `Double.NaN`.

void	init ()

FunctionOfMultipleMeansTally	clone ()
	Clones this object. More...

Public Member Functions inherited from StatProbe
abstract void	init ()
	Initializes the statistical collector.

void	setName (String name)
	Sets the name of this statistical collector to `name`.

String	getName ()
	Returns the name associated with this probe, or `null` if no name was specified upon construction. More...

double	min ()
	Returns the smallest value taken by the variable since the last initialization of this probe. More...

double	max ()
	Returns the largest value taken by the variable since the last initialization of this probe. More...

double	sum ()
	Returns the sum cumulated so far for this probe. More...

abstract double	average ()
	Returns the average for this collector. More...

abstract String	report ()
	Returns a string containing a report for this statistical collector. More...

abstract String	shortReport ()
	Formats and returns a short, one-line report about this statistical probe. More...

abstract String	shortReportHeader ()
	Returns a string containing the name of the values returned in the report strings. More...

boolean	isBroadcasting ()
	Determines if this statistical probe is broadcasting observations to registered observers. More...

void	setBroadcasting (boolean b)
	Instructs the probe to turn its broadcasting ON or OFF. More...

boolean	isCollecting ()
	Determines if this statistical probe is collecting values. More...

void	setCollecting (boolean b)
	Turns ON or OFF the collection of statistical observations. More...

void	addObservationListener (ObservationListener l)
	Adds the observation listener `l` to the list of observers of this statistical probe. More...

void	removeObservationListener (ObservationListener l)
	Removes the observation listener `l` from the list of observers of this statistical probe. More...

void	clearObservationListeners ()
	Removes all observation listeners from the list of observers of this statistical probe.

void	notifyListeners (double x)
	Notifies the observation `x` to all registered observers if broadcasting is ON. More...

StatProbe	clone () throws CloneNotSupportedException

Additional Inherited Members
Static Public Member Functions inherited from StatProbe
static String	report (String globalName, StatProbe[] probes)
	Formats short reports for each statistical probe in the array `probes` while aligning the probes’ names. More...

static String	report (String globalName, Iterable<? extends StatProbe > probes)
	Equivalent to #report(String,StatProbe[]), except that `probes` is an Iterable object instead of an array. More...

Protected Attributes inherited from FunctionOfMultipleMeansTally
MultivariateFunction	func

CIType	confidenceInterval = CIType.CI_NONE

double	level = 0.95

Protected Attributes inherited from StatProbe
String	name

double	maxValue

double	minValue

double	sumValue

boolean	collect = true

boolean	broadcast = false

boolean	showNobs = true

Detailed Description

Represents a function of multiple means tally for an estimator with linear control variables.

This extends the function of multiple means tally to use a function \(h(\boldsymbol{\mu}, \boldsymbol{\nu})=g(\boldsymbol{\mu})-\boldsymbol{\beta}_{\mathrm{f}}^{\mathsf{t}}(\mathbf{C}- \boldsymbol{\nu})\), where \(\boldsymbol{\beta}_{\mathrm{f}}=(\beta_{\mathrm{f},0},…,\beta_{\mathrm{f},q-1})\) is a \(q\)-dimensional vector. One must provide a definition for \(g(\boldsymbol{\mu})\) and its gradient \(\nabla g(\boldsymbol{\mu})\) through an implementation of the umontreal.ssj.util.MultivariateFunction interface.

Constructor & Destructor Documentation

◆ FunctionOfMultipleMeansTallyWithCV() [1/2]

FunctionOfMultipleMeansTallyWithCV	(	MultivariateFunction	funcNoCV,
		int	p,
		int	q
	)

Creates a new function of multiple means tally for a function funcNoCV of p variables, and with q control variables.

The constructed tally requires vectors of p+q values as observations.

Parameters

funcNoCV	the function used.
p	the number of dimensions.
q	the number of control variables.

◆ FunctionOfMultipleMeansTallyWithCV() [2/2]

FunctionOfMultipleMeansTallyWithCV	(	MultivariateFunction	funcNoCV,
		ListOfTalliesWithCV< Tally >	l
	)

Constructs a new function of multiple means tally with control variables from the list of tallies l, and the function funcNoCV.

Parameters

funcNoCV	the function being computed.
l	the list of tallies used.

Member Function Documentation

◆ clone()

FunctionOfMultipleMeansTallyWithCV clone ( )

Clones this object and the function which is stored inside.

This clones the internal list of tallies as well as each tally in this list.

◆ estimateBeta()

void estimateBeta ( )

Uses the sample averages and covariances obtained from the internal list of tallies to estimate the \(\boldsymbol{\beta}_{\mathrm{f}}\) vector minimizing the variance of \(h(\bar{\mathbf{X}}_n, \mathbf{C})\).

The asymptotically optimal vector is

\[ \boldsymbol{\beta}_{\mathrm{f}}^* = \boldsymbol{\Sigma}_{\mathrm{C}}^{-1}\boldsymbol{\Sigma}_{\mathrm{CX}}\nabla g(\boldsymbol{\mu}). \]

The method estimates this vector by replacing every quantity by its estimator. It first uses umontreal.ssj.stat.list.lincv.ListOfTalliesWithCV.estimateBeta from the internal list of tallies to get a \(\boldsymbol{\beta}\) matrix, and calls estimateBetaFromMatrix(DoubleMatrix2D) to get the \(\boldsymbol{\beta}_{\mathrm{f}}\) vector.

◆ estimateBetaFromMatrix()

void estimateBetaFromMatrix ( DoubleMatrix2D mbeta )

Multiples the given \(q\times p\) matrix by the gradient \(\nabla g(\bar{\mathbf{X}}_n)\) to get an estimate of the \(\boldsymbol{\beta}_{\mathrm{f}}^*\) vector minimizing the variance.

The matrix is usually obtained by umontreal.ssj.stat.list.lincv.ListOfTalliesWithCV.estimateBeta. from the internal list of tallies.

Parameters

mbeta the \(\boldsymbol{\beta}\) matrix.

◆ getBeta() [1/2]

double getBeta ( int i )

Returns the value of \(\beta_{\mathrm{f},i}\).

This is set to 0 by default.

Parameters

i	the index of the control variable.

Returns: the value of \(\beta_{\mathrm{f},i}\).

◆ getBeta() [2/2]

double [] getBeta ( )

Returns the \(\boldsymbol{\beta}_{\mathrm{f}}\) vector.

By default, this is set to an array of 0’s.

Returns: the values of \(\boldsymbol{\beta}_{\mathrm{f}}\).

◆ getDimensionWithoutCV()

int getDimensionWithoutCV ( )

Returns the dimension of this tally excluding the control variables.

This corresponds to the result of umontreal.ssj.stat.FunctionOfMultipleMeansTally.getDimension minus the number of control variables.

Returns: the dimension without control variables.

◆ getExpectedValue()

double getExpectedValue ( int i )

Gets the expected value of the \(i\)th component of \(\boldsymbol{\nu}\).

This is set to 0 by default, and is equivalent to calling getExpectedValue on the internal list of tallies.

Parameters

i	the component of \(\boldsymbol{\nu}\) to query.

Returns: the queried expectation.

◆ getExpectedValues()

double [] getExpectedValues ( )

Gets an array containing the vector \(\boldsymbol{\nu}\).

This returns an array of 0’s by default, and is equivalent to calling getExpectedValues on the internal list of tallies.

Returns: the vector \(\boldsymbol{\nu}\).

◆ getFunctionWithoutCV()

MultivariateFunction getFunctionWithoutCV ( )

Returns the implementation computing the function \(g(\boldsymbol{\mu})\).

This differs from getFunction which returns \(h(\boldsymbol{\mu}, \boldsymbol{\nu})\).

Returns: the implementation computing the function \(g(\boldsymbol{\mu})\).

◆ getNumControlVariables()

int getNumControlVariables ( )

Returns the number of control variables being used.

Returns: the number of control variables.

◆ setBeta() [1/2]

void setBeta	(	int	i,
		double	b
	)

Sets the value of \(\beta_{\mathrm{f},i}\).

Parameters

i	the index of the control variable.
b	the value of \(\beta_{\mathrm{f},i}\).

◆ setBeta() [2/2]

void setBeta ( double [] beta )

Sets the value of \(\boldsymbol{\beta}_{\mathrm{f}}\) vector to beta.

The given array must have length \(q\).

Parameters

beta	the new \(\boldsymbol{\beta}_{\mathrm{f}}\) vector.

◆ setExpectedValue()

void setExpectedValue	(	int	i,
		double	e
	)

Sets the expected value of the \(i\)th component of \(\boldsymbol{\nu}\) to e.

This is equivalent to calling setExpectedValue on the internal list of tallies.

Parameters

i	the component of \(\boldsymbol{\nu}\) to set.
e	the new value of the expectation.

◆ setExpectedValues()

void setExpectedValues ( double [] exp )

Sets the vector \(\boldsymbol{\nu}\) to to given array exp.

The length of exp must be \(q\). This is equivalent to calling setExpectedValues on the internal list of tallies.

Parameters

exp	the new \(\boldsymbol{\nu}\) vector.

The documentation for this class was generated from the following file:

FunctionOfMultipleMeansTallyWithCV.java

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ FunctionOfMultipleMeansTallyWithCV() [1/2]

◆ FunctionOfMultipleMeansTallyWithCV() [2/2]

Member Function Documentation

◆ clone()

◆ estimateBeta()

◆ estimateBetaFromMatrix()

◆ getBeta() [1/2]

◆ getBeta() [2/2]

◆ getDimensionWithoutCV()

◆ getExpectedValue()

◆ getExpectedValues()

◆ getFunctionWithoutCV()

◆ getNumControlVariables()

◆ setBeta() [1/2]

◆ setBeta() [2/2]

◆ setExpectedValue()

◆ setExpectedValues()