List of inverseCumulativeProbability() Examples

Examples of inverseCumulativeProbability()

org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns 0 for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.IntegerDistribution.inverseCumulativeProbability()
For this distribution, X, this method returns the largest x such that P(X ≤ x) <= p.
Note that this definition implies:
- If there is a minimum value, m, with positive probability under (the density of) X, then m - 1 is returned by inverseCumulativeProbability(0). If there is no such value m, Integer.MIN_VALUE is returned.
- If there is a maximum value, M, such that P(X ≤ M) =1, then M is returned by inverseCumulativeProbability(1). If there is no such value, M, Integer.MAX_VALUE is returned.
@param p the cumulative probability. @return the largest x such that P(X ≤ x) <= p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not between 0 and 1 (inclusive)
org.apache.commons.math.distribution.NormalDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.PoissonDistributionImpl.inverseCumulativeProbability()
org.apache.commons.math.distribution.TDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.TDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.ZipfDistributionImpl.inverseCumulativeProbability()
org.apache.commons.math3.distribution.BinomialDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.ExponentialDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()
{@inheritDoc} @since 3.2
org.apache.commons.math3.distribution.RealDistribution.inverseCumulativeProbability()
Computes the quantile function of this distribution. For a random variable {@code X} distributed according to this distribution, thereturned value is
- inf{x in R | P(X<=x)>= p} for {@code 0 < p <= 1},
- inf{x in R | P(X<=x)> 0} for {@code p = 0}.
@param p the cumulative probability @return the smallest {@code p}-quantile of this distribution (largest 0-quantile for {@code p = 0}) @throws OutOfRangeException if {@code p < 0} or {@code p> 1}
org.apache.commons.math3.distribution.TDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.WeibullDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p == 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.

Examples of org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()

    time = System.currentTimeMillis();
    int ccount = 0;
    int hypotheses = ((toksAboveThresh-1) * (toksAboveThresh-2)) / 2;


    ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
    double upperThreshold = csd.inverseCumulativeProbability(1.0 - (fdr / hypotheses));
    double lowerThreshold = csd.inverseCumulativeProbability(1.0 - fdr);
    Map<String,String> reports = new HashMap<String,String>();
    int totalViable = 0;
    for(Long c : collocs) {
      if(c == colloc) {

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()

    int ccount = 0;
    int hypotheses = ((toksAboveThresh-1) * (toksAboveThresh-2)) / 2;


    ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
    double upperThreshold = csd.inverseCumulativeProbability(1.0 - (fdr / hypotheses));
    double lowerThreshold = csd.inverseCumulativeProbability(1.0 - fdr);
    Map<String,String> reports = new HashMap<String,String>();
    int totalViable = 0;
    for(Long c : collocs) {
      if(c == colloc) {
        count++;

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()

   * @param args
   */
  public static void main(String[] args) throws Exception {
    if(false) {
      ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
      System.out.println(csd.inverseCumulativeProbability(1.0 - (0.05 / 1000000000)));
      System.out.println(csd.inverseCumulativeProbability(0.05));
      return;
    }
    if(false) {
      System.out.println(3 << 16);

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()

   */
  public static void main(String[] args) throws Exception {
    if(false) {
      ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
      System.out.println(csd.inverseCumulativeProbability(1.0 - (0.05 / 1000000000)));
      System.out.println(csd.inverseCumulativeProbability(0.05));
      return;
    }
    if(false) {
      System.out.println(3 << 16);
      int [] r = decodeCollocation(encodeCollocation(1000, 10000));

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()

      System.out.println(r[0] + "\t" + r[1]);
      
      ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(3);
      for(int i=0;i<50;i++) {
        double p = Math.pow(10.0, -i);
        System.out.println(p + "\t" + csd.inverseCumulativeProbability(p));
      }
      
      return;
    }

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()

    time = System.currentTimeMillis();
    int ccount = 0;
    int hypotheses = ((toksAboveThresh-1) * (toksAboveThresh-2)) / 2;


    ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
    double upperThreshold = csd.inverseCumulativeProbability(1.0 - (fdr / hypotheses));
    double lowerThreshold = csd.inverseCumulativeProbability(1.0 - fdr);
    Map<String,String> reports = new HashMap<String,String>();
    int totalViable = 0;
    for(Long c : collocs) {
      if(c == colloc) {

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()

    int ccount = 0;
    int hypotheses = ((toksAboveThresh-1) * (toksAboveThresh-2)) / 2;


    ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
    double upperThreshold = csd.inverseCumulativeProbability(1.0 - (fdr / hypotheses));
    double lowerThreshold = csd.inverseCumulativeProbability(1.0 - fdr);
    Map<String,String> reports = new HashMap<String,String>();
    int totalViable = 0;
    for(Long c : collocs) {
      if(c == colloc) {
        count++;

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()

   * @param args
   */
  public static void main(String[] args) throws Exception {
    if(false) {
      ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
      System.out.println(csd.inverseCumulativeProbability(1.0 - (0.05 / 1000000000)));
      System.out.println(csd.inverseCumulativeProbability(0.05));
      return;
    }
    if(false) {
      System.out.println(3 << 16);

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()

   */
  public static void main(String[] args) throws Exception {
    if(false) {
      ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(1);
      System.out.println(csd.inverseCumulativeProbability(1.0 - (0.05 / 1000000000)));
      System.out.println(csd.inverseCumulativeProbability(0.05));
      return;
    }
    if(false) {
      System.out.println(3 << 16);
      int [] r = decodeCollocation(encodeCollocation(1000, 10000));

View Full Code Here

Examples of org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()

      System.out.println(r[0] + "\t" + r[1]);
      
      ChiSquaredDistribution csd = new ChiSquaredDistributionImpl(3);
      for(int i=0;i<50;i++) {
        double p = Math.pow(10.0, -i);
        System.out.println(p + "\t" + csd.inverseCumulativeProbability(p));
      }
      
      return;
    }

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.