package com.imsl.test.example.stat.distributions;

import com.imsl.math.*;
import com.imsl.stat.Random;
import com.imsl.stat.distributions.*;

/**
 * <p>
 * Estimates the parameters of the normal probability distribution.</p>
 * <p>
 * This example obtains maximum likelihood estimates for the parameters of a
 * normal (Gaussian) distribution.</p>
 * 
 * @see <a href="MaximumLikelihoodEstimationEx3.java">Code</a>
 * @see <a href="doc-files/MaximumLikelihoodEstimationEx3.out">Output</a>
 */
public class MaximumLikelihoodEstimationEx3 {

    public static void main(String[] args) throws Exception {
        int n = 100;
        double[] sample = new double[n];
        double mean = 25;
        double stdev = 1.5;
        Random rand = new Random(123457);

        for (int i = 0; i < n; i++) {
            sample[i] = stdev * rand.nextNormal() + mean;
        }

        NormalPD norm = new NormalPD();
        double[] exactMLEs = norm.getMLEs(sample);

        MaximumLikelihoodEstimation mle
                = new MaximumLikelihoodEstimation(sample, norm, 2, 1);

        mle.compute();

        double[] parameterEstimates = mle.getEstimates();
        double[][] hessian = mle.getHessian();
        double[] stdErrs = mle.getStandardErrors();
        double mLogLikelihood = mle.getMinusLogLikelihood();

        PrintMatrixFormat pmf = new PrintMatrixFormat();
        pmf.setNumberFormat(new java.text.DecimalFormat("0.0000000"));

        PrintMatrix pm = new PrintMatrix("Analytic MLEs");
        pm.print(pmf, exactMLEs);

        pm.setTitle("Numerical MLEs");
        pm.print(pmf, parameterEstimates);

        pm.setTitle("hessian");
        pm.print(pmf, hessian);

        pm.setTitle("standard errors");
        pm.print(pmf, stdErrs);

        System.out.printf("Minus log likelihood: %5.2f\n", mLogLikelihood);
    }
}