jhpro.upropog

Class UPropogate

java.lang.Object

jhpro.upropog.UPropogate

public class UPropogate
extends java.lang.Object

Propagation of uncertainties (or errors) for a complex function using a Monte Carlo method. We assume uncertainties are distributed according to a normal distribution (Gaussian).

Constructor Summary

Constructors

Constructor and Description
UPropogate(FNon f, java.util.ArrayList par, java.util.ArrayList par_err, java.util.ArrayList var, java.util.ArrayList var_err, int NTOT) Initialize propagation of uncertainties (or errors).
UPropogate(FNon f, double[] par, double[] par_err, double[] var, double[] var_err, int NTOT) Initialize propagation of uncertainties (or errors).

Method Summary

Modifier and Type	Method and Description
H1D	getHisto() Get a histogram representing a spread of the final function from Monte Carlo simulation.
double	getStd() Get total uncertainty as standard deviation.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

UPropogate

public UPropogate(FNon f,
                  double[] par,
                  double[] par_err,
                  double[] var,
                  double[] var_err,
                  int NTOT)

Initialize propagation of uncertainties (or errors). The list with errors include uncertainties on the parameters plus on the function avariable. They can be zero if you do not want any uncertainties.

Parameters:

f - input function

par - a list with parameters values

par_err - a list with uncertainties for parameters

var - a list with variables around which function should be evaluated

var_err - a list with uncertainties for variables (can be zero) (can be all zero)

NTOT - total number of Monte Carlo events. the more is the better.

UPropogate

public UPropogate(FNon f,
                  java.util.ArrayList par,
                  java.util.ArrayList par_err,
                  java.util.ArrayList var,
                  java.util.ArrayList var_err,
                  int NTOT)

Initialize propagation of uncertainties (or errors). The list with errors include uncertainties on the parameters plus on the function avariable. They can be zero if you do not want any uncertainties.

Parameters:

f - input function

par - a list with parameters values

par_err - a list with uncertainties for parameters

var - a list with variables around which function should be evaluated

var_err - a list with uncertainties for variables (can be zero) (can be all zero)

NTOT - total number of Monte Carlo events. the more is the better.

Method Detail

getStd

public double getStd()

Get total uncertainty as standard deviation.

Returns:

total uncertainty

getHisto

public H1D getHisto()

Get a histogram representing a spread of the final function from Monte Carlo simulation.

Returns:

histogram from Monte Carlo simulation