RoundedLogNormal.html

RoundedLogNormal.java

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package blog.distrib;

import java.util.*;
import blog.*;

Distribution over positive integers for a random variable X = round(Y), where Y has a log normal distribution. This means Z = log(Y) has a normal distribution. A RoundedLogNormal has two parameters, the mean and the variance. For consistency with Hanna's code, we use the mean of Y and the variance of Z.


public class RoundedLogNormal extends AbstractCondProbDistrib {

Creates a RoundedLogNormal distribution where Y has the given mean and log(Y) has the given variance.


    public RoundedLogNormal(double mean, double varianceOfLog) {
	this.mean = mean;
	this.varianceOfLog = varianceOfLog;
	zDistrib = new UnivarGaussian(Math.log(mean), varianceOfLog);
    }

Creates a RoundedLogNormal distribution with the given parameters. This method expects two parameters of class Number, namely the mean of Y and the variance of log(Y).


    public RoundedLogNormal(List params) {
	if (params.size() != 2) {
	    throw new IllegalArgumentException
		("RoundedLogNormal expects two parameters: the mean of Y "
		 + "and the variance of log(Y).");
	}

	mean = ((Number) params.get(0)).doubleValue();
	varianceOfLog = ((Number) params.get(1)).doubleValue();
	zDistrib = new UnivarGaussian(Math.log(mean), varianceOfLog);
    }

Returns the probability of the given value under this distribution. Expects no arguments.


    public double getProb(List args, Object value) {
	return Math.exp(getLogProb(args, value));
    }

Returns the log probability of the given value under this distribution. Expects no arguments.


    public double getLogProb(List args, Object value) {
	if (!args.isEmpty()) {
	    throw new IllegalArgumentException
		("RoundedLogNormal expects no arguments.");
	}
	return getLogProb(((Integer) value).intValue());
    }

Returns the log probability that X=n. Note that X gets the value n if Y is between n - 0.5 and n + 0.5, which means Z is between log(n - 0.5) and log(n + 0.5). So we should integrate the density of Z between those two values. To avoid computing the integral, we approximate this by taking the density of Z at log(n) and multiplying it by log(n + 0.5) - log(n - 0.5).


    public double getLogProb(int n) {
	if (n <= 0) {
	    return Double.NEGATIVE_INFINITY;
	}

	double intervalWidth = Math.log(n + 0.5) - Math.log(n - 0.5);
	return Math.log(intervalWidth) + zDistrib.getLogProb(Math.log(n));
    }

    public Object sampleVal(List args, Type childType) {
	if (!args.isEmpty()) {
	    throw new IllegalArgumentException
		("RoundedLogNormal expects no arguments.");
	}

	double z = zDistrib.sampleVal();
	double y = Math.exp(z);
	long x = Math.round(y);
	if ((x < Integer.MIN_VALUE) || (x > Integer.MAX_VALUE)) {
	    throw new RuntimeException
		("Value sampled from RoundedLogNormal distribution is "
		 + "too great in magnitude to be represented as an Integer.");
	}
	return new Integer((int) x);
    }

    double mean;
    double varianceOfLog;

    UnivarGaussian zDistrib;
}

This file was generated on Tue Jun 08 17:53:47 PDT 2010 from file RoundedLogNormal.java
by the ilog.language.tools.Hilite Java tool written by Hassan Aït-Kaci