/*
* NegativeBinomialDistributionModel.java
*
* Copyright (c) 2002-2016 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.AbstractModel;
import dr.inference.model.Model;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import dr.inferencexml.distribution.NegativeBinomialDistributionModelParser;
import dr.math.UnivariateFunction;
import dr.math.distributions.NegativeBinomialDistribution;
import org.w3c.dom.Document;
import org.w3c.dom.Element;
/**
* A class that acts as a model for Negative Binomially distributed data.
*
* @author Andrew Rambaut
* @version $Id$
*/
public class NegativeBinomialDistributionModel extends AbstractModel implements ParametricDistributionModel {
/**
* Constructor.
*/
public NegativeBinomialDistributionModel(Variable<Double> mean, Variable<Double> alpha) {
super(NegativeBinomialDistributionModelParser.NEGATIVE_BINOMIAL_DISTRIBUTION_MODEL);
this.mean = mean;
addVariable(mean);
mean.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
this.alpha = alpha;
addVariable(alpha);
alpha.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, 0.0, 1));
}
// *****************************************************************
// Interface Distribution
// *****************************************************************
public double pdf(double x) {
return NegativeBinomialDistribution.pdf(x, mean(), alpha());
}
public double logPdf(double x) {
return NegativeBinomialDistribution.logPdf(x, mean(), alpha());
}
public double cdf(double x) {
return NegativeBinomialDistribution.cdf(x, mean(), alpha());
}
public double quantile(double y) {
throw new RuntimeException("Not implemented.");
}
public double mean() {
return mean.getValue(0);
}
public double alpha() {
return alpha.getValue(0);
}
public double variance() {
throw new RuntimeException("Not implemented!");
}
public final UnivariateFunction getProbabilityDensityFunction() {
return pdfFunction;
}
private final UnivariateFunction pdfFunction = new UnivariateFunction() {
public final double evaluate(double x) {
return pdf(x);
}
public final double getLowerBound() {
return 0.0;
}
public final double getUpperBound() {
return Double.POSITIVE_INFINITY;
}
};
// *****************************************************************
// Interface DensityModel
// *****************************************************************
@Override
public double logPdf(double[] x) {
return logPdf(x[0]);
}
@Override
public Variable<Double> getLocationVariable() {
return mean;
}
// *****************************************************************
// Interface Model
// *****************************************************************
public void handleModelChangedEvent(Model model, Object object, int index) {
// no intermediates need to be recalculated...
}
protected final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
fireModelChanged();
}
protected void storeState() {
} // no additional state needs storing
protected void restoreState() {
} // no additional state needs restoring
protected void acceptState() {
} // no additional state needs accepting
public Element createElement(Document document) {
throw new RuntimeException("Not implemented!");
}
// **************************************************************
// Private instance variables
// **************************************************************
private final Variable<Double> mean;
private final Variable<Double> alpha;
}