Example usage for org.apache.mahout.math.jet.random AbstractContinousDistribution subclass-usage

Introduction

In this page you can find the example usage for org.apache.mahout.math.jet.random AbstractContinousDistribution subclass-usage.

Usage

From source file com.mapr.stats.AbstractBayesianDistribution.java

/**
 * Expresses the common characteristics of a two-level distribution in which
 * the higher level distribution describes a prior distribution of parameters
 * for the lower level distribution.  Generically speaking, we have
 * <pre>
 *   \theta ~ p1(\alpha)

From source file com.mapr.stats.BetaDistribution.java

/**
 * Sample from a beta distribution.
 */
public class BetaDistribution extends AbstractContinousDistribution {
    private final Gamma gAlpha;
    private final Gamma gBeta;

From source file com.mapr.stats.DistributionWithMean.java

/**
 * Represents a distribution that knows it's own mean.
 */
public class DistributionWithMean extends AbstractContinousDistribution
        implements Comparable<DistributionWithMean> {
    private AbstractContinousDistribution delegate;

From source file com.mapr.stats.random.AbstractBayesianDistribution.java

/**
 * Expresses the common characteristics of a two-level distribution in which
 * the higher level distribution describes a prior distribution of parameters
 * for the lower level distribution.  Generically speaking, we have
 * \[
 *   \theta ~ p_1(\alpha) \\

From source file com.mapr.stats.random.BetaDistribution.java

/**
 * Sample from a beta distribution.
 *
 * The beta distribution has PDF of
 * \[
 * p(x \mid \alpha, \alpha) = {\frac {\Gamma(\alpha+\beta)} {\Gamma(\alpha) \Gamma(\beta}} x^{\alpha-1} (1-x)^{\beta-1}

From source file com.mapr.stats.random.DistributionWithMean.java

/**
 * Represents a distribution that knows it's own mean.
 */
public class DistributionWithMean extends AbstractContinousDistribution
        implements Comparable<DistributionWithMean> {
    private AbstractContinousDistribution delegate;