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Luc Maisonobe commented on MATH-364: ------------------------------------ I'm not sure I understand. If adding an erfc(x1, x2) method that compute the difference erc(x1)-erfc(x2) accurately, then it seems a good improvement. > Make Erf more precise in the tails by providing erfc > ---------------------------------------------------- > > Key: MATH-364 > URL: https://issues.apache.org/jira/browse/MATH-364 > Project: Commons Math > Issue Type: Improvement > Affects Versions: 1.1, 1.2, 2.0, 2.1 > Reporter: Christian Winter > Priority: Minor > Fix For: 3.0 > > > First I want to thank Phil Steitz for making Erf stable in the tails through > adjusting the choices in calculating the regularized gamma functions, see > [Math-282|https://issues.apache.org/jira/browse/MATH-282]. However, the > precision of Erf in the tails is limitted to fixed point precision because of > the closeness to +/-1.0, although the Gamma class could provide much more > accuracy. Thus I propose to add the methods erfc(double) and erf(double, > double) to the class Erf: > {code:borderStyle=solid} > /** > * Returns the complementary error function erfc(x). > * @param x the value > * @return the complementary error function erfc(x) > * @throws MathException if the algorithm fails to converge > */ > public static double erfc(double x) throws MathException { > double ret = Gamma.regularizedGammaQ(0.5, x * x, 1.0e-15, 10000); > if (x < 0) { > ret = -ret; > } > return ret; > } > /** > * Returns the difference of the error function values of x1 and x2. > * @param x1 the first bound > * @param x2 the second bound > * @return erf(x2) - erf(x1) > * @throws MathException > */ > public static double erf(double x1, double x2) throws MathException { > if(x1>x2) > return erf(x2, x1); > if(x1==x2) > return 0.0; > > double f1 = erf(x1); > double f2 = erf(x2); > > if(f2 > 0.5) > if(f1 > 0.5) > return erfc(x1) - erfc(x2); > else > return (0.5-erfc(x2)) + (0.5-f1); > else > if(f1 < -0.5) > if(f2 < -0.5) > return erfc(-x2) - erfc(-x1); > else > return (0.5-erfc(-x1)) + (0.5+f2); > else > return f2 - f1; > } > {code} > Further this can be used to improve the NormalDistributionImpl through > {code:borderStyle=solid} > @Override > public double cumulativeProbability(double x0, double x1) throws > MathException { > return 0.5 * Erf.erf( > (x0 - getMean()) / (getStandardDeviation() * sqrt2), > (x1 - getMean()) / (getStandardDeviation() * sqrt2) ); > } > {code} -- This message is automatically generated by JIRA. For more information on JIRA, see: http://www.atlassian.com/software/jira