edu.mit.util.stats
Class Stats

java.lang.Object
  extended by edu.mit.util.stats.Stats

public class Stats
extends Object


Field Summary
static double MAX_LOG_DISPERSION
           
 
Constructor Summary
Stats()
           
 
Method Summary
static double computeDispersionGradient(int len, double mean_len, double log_dispersion, FastDigamma digamma)
           
static double[] computeLogMultinomial(double[] counts, double prior)
           
static double[] computeMultinomial(double[] counts, double prior)
          computeMultinomial builds the expected multinomial given counts and a symmetric dirichlet theta = argmax_theta p(theta | counts, prior) = argmax_theta p(counts | theta) p(theta | prior) this is in closed form due to conjugacy
static
<T> List<List<T>>
generateOrderings(List<T> stuff)
           
static double kldiv(double[] distrib1, double[] distrib2)
          Computes the KL divergence of the distribution.
static double logProbLogMultinomial(double[] x, double[] loga)
           
static double logProbMultinomial(double[] x, double[] a)
          computes the log-probability of a bag-of-words observation x, given the multinomial probability distribution a
static void main(String[] argv)
           
static double myLogGammaPdf(double x, double mean, double variance)
           
static double myLogNBinPdf(int k, double r, double p)
           
static double myLogNBinPdf2(int z, double m, double k)
           
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

MAX_LOG_DISPERSION

public static double MAX_LOG_DISPERSION
Constructor Detail

Stats

public Stats()
Method Detail

kldiv

public static double kldiv(double[] distrib1,
                           double[] distrib2)
Computes the KL divergence of the distribution. Actually it's the skewed divergence, from Lee 2001


generateOrderings

public static <T> List<List<T>> generateOrderings(List<T> stuff)

myLogNBinPdf

public static double myLogNBinPdf(int k,
                                  double r,
                                  double p)

myLogNBinPdf2

public static double myLogNBinPdf2(int z,
                                   double m,
                                   double k)

myLogGammaPdf

public static double myLogGammaPdf(double x,
                                   double mean,
                                   double variance)

computeMultinomial

public static double[] computeMultinomial(double[] counts,
                                          double prior)
computeMultinomial builds the expected multinomial given counts and a symmetric dirichlet theta = argmax_theta p(theta | counts, prior) = argmax_theta p(counts | theta) p(theta | prior) this is in closed form due to conjugacy


computeLogMultinomial

public static double[] computeLogMultinomial(double[] counts,
                                             double prior)

logProbMultinomial

public static double logProbMultinomial(double[] x,
                                        double[] a)
computes the log-probability of a bag-of-words observation x, given the multinomial probability distribution a


logProbLogMultinomial

public static double logProbLogMultinomial(double[] x,
                                           double[] loga)

computeDispersionGradient

public static double computeDispersionGradient(int len,
                                               double mean_len,
                                               double log_dispersion,
                                               FastDigamma digamma)

main

public static void main(String[] argv)


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