/*
* BinomialLikelihood.java
*
* Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
*
* This file is part of BEAST.
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership and licensing.
*
* BEAST is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* BEAST is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with BEAST; if not, write to the
* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
* Boston, MA 02110-1301 USA
*/
package dr.inference.distribution;
import dr.inference.model.AbstractModelLikelihood;
import dr.inference.model.Model;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import dr.math.Binomial;
import org.w3c.dom.Document;
import org.w3c.dom.Element;
/**
* A class that returns the log likelihood of a set of data (statistics)
* being distributed according to a binomial distribution.
*
* @author Alexei Drummond
* @version $Id: BinomialLikelihood.java,v 1.5 2005/05/24 20:25:59 rambaut Exp $
*/
public class BinomialLikelihood extends AbstractModelLikelihood {
public static final String BINOMIAL_LIKELIHOOD = "binomialLikelihood";
public BinomialLikelihood(Parameter trialsParameter, Parameter proportionParameter, Parameter countsParameter,
boolean onLogitScale) {
super(BINOMIAL_LIKELIHOOD);
this.trialsParameter = trialsParameter;
this.proportionParameter = proportionParameter;
this.countsParameter = countsParameter;
addVariable(trialsParameter);
addVariable(proportionParameter);
addVariable(countsParameter);
this.onLogitScale = onLogitScale;
}
// **************************************************************
// Likelihood IMPLEMENTATION
// **************************************************************
public Model getModel() {
return this;
}
/**
* Calculate the log likelihood of the current state.
*
* @return the log likelihood.
*/
public double getLogLikelihood() {
// Get first success probability statistics
SufficientStatistics ss = new SufficientStatistics(proportionParameter.getParameterValue(0), onLogitScale);
if (ss.outOfBounds) return Double.NEGATIVE_INFINITY;
final boolean hasMultipleProportions = proportionParameter.getDimension() > 1;
double logL = 0.0;
for (int i = 0; i < trialsParameter.getDimension(); i++) {
if (hasMultipleProportions && i > 0) {
ss = new SufficientStatistics(proportionParameter.getParameterValue(i), onLogitScale);
if (ss.outOfBounds) return Double.NEGATIVE_INFINITY;
}
int trials = (int) Math.round(trialsParameter.getParameterValue(i));
int counts = (int) Math.round(countsParameter.getParameterValue(i));
if (counts > trials) return Double.NEGATIVE_INFINITY;
logL += binomialLogLikelihood(trials, counts, ss.logP, ss.log1MinusP);
}
return logL;
}
class SufficientStatistics {
double logP;
double log1MinusP;
boolean outOfBounds;
SufficientStatistics(double theta, boolean onLogitScale) {
if (onLogitScale) {
// e(x) / (1 + e(x)) and 1 / (1 + e(x))
final double logDenominator = Math.log(1.0 + Math.exp(theta));
logP = theta - logDenominator;
log1MinusP = -logDenominator;
outOfBounds = false;
} else {
logP = Math.log(theta);
log1MinusP = Math.log(1.0 - theta);
outOfBounds = (theta <= 0 || theta >= 1);
}
}
}
public void makeDirty() {
}
public void acceptState() {
// DO NOTHING
}
public void restoreState() {
// DO NOTHING
}
public void storeState() {
// DO NOTHING
}
protected void handleModelChangedEvent(Model model, Object object, int index) {
// DO NOTHING
}
protected final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
// DO NOTHING
}
/**
* @return the binomial likelihood of obtaining the gicen count in the given number of trials,
* when the log of the probability is logP.
*/
private double binomialLogLikelihood(int trials, int count, double logP, double log1MinusP) {
return Math.log(Binomial.choose(trials, count)) + (logP * count) + (log1MinusP * (trials - count));
}
// **************************************************************
// XMLElement IMPLEMENTATION
// **************************************************************
public Element createElement(Document d) {
throw new RuntimeException("Not implemented yet!");
}
Parameter trialsParameter;
Parameter proportionParameter;
Parameter countsParameter;
private final boolean onLogitScale;
}