R1MultivariateNormalConvolutionEngine.java
package org.drip.measure.bayesian;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 Lakshmi Krishnamurthy
* Copyright (C) 2018 Lakshmi Krishnamurthy
* Copyright (C) 2017 Lakshmi Krishnamurthy
* Copyright (C) 2016 Lakshmi Krishnamurthy
*
* This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
* asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
* analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
* equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
* numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
* and computational support.
*
* https://lakshmidrip.github.io/DROP/
*
* DROP is composed of three modules:
*
* - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
* - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
* - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
*
* DROP Product Core implements libraries for the following:
* - Fixed Income Analytics
* - Loan Analytics
* - Transaction Cost Analytics
*
* DROP Portfolio Core implements libraries for the following:
* - Asset Allocation Analytics
* - Asset Liability Management Analytics
* - Capital Estimation Analytics
* - Exposure Analytics
* - Margin Analytics
* - XVA Analytics
*
* DROP Computational Core implements libraries for the following:
* - Algorithm Support
* - Computation Support
* - Function Analysis
* - Model Validation
* - Numerical Analysis
* - Numerical Optimizer
* - Spline Builder
* - Statistical Learning
*
* Documentation for DROP is Spread Over:
*
* - Main => https://lakshmidrip.github.io/DROP/
* - Wiki => https://github.com/lakshmiDRIP/DROP/wiki
* - GitHub => https://github.com/lakshmiDRIP/DROP
* - Repo Layout Taxonomy => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
* - Javadoc => https://lakshmidrip.github.io/DROP/Javadoc/index.html
* - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
* - Release Versions => https://lakshmidrip.github.io/DROP/version.html
* - Community Credits => https://lakshmidrip.github.io/DROP/credits.html
* - Issues Catalog => https://github.com/lakshmiDRIP/DROP/issues
* - JUnit => https://lakshmidrip.github.io/DROP/junit/index.html
* - Jacoco => https://lakshmidrip.github.io/DROP/jacoco/index.html
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
*
* You may obtain a copy of the License at
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/**
* <i>R1NormalConvolutionEngine</i> implements the Engine that generates the Joint/Posterior Distribution
* from the Prior and the Conditional Joint R<sup>1</sup> Multivariate Normal Distributions.
*
* <br><br>
* <ul>
* <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
* <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/README.md">R<sup>d</sup> Continuous/Discrete Probability Measures</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/bayesian/README.md">Prior, Conditional, Posterior Theil Bayesian</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class R1MultivariateNormalConvolutionEngine implements org.drip.measure.bayesian.R1MultivariateConvolutionEngine {
/**
* Empty R1MultivariateNormalConvolutionEngine Construction
*/
public R1MultivariateNormalConvolutionEngine()
{
}
@Override public org.drip.measure.bayesian.R1MultivariateConvolutionMetrics process (
final org.drip.measure.continuous.R1Multivariate r1mPrior,
final org.drip.measure.continuous.R1Multivariate r1mUnconditional,
final org.drip.measure.continuous.R1Multivariate r1mConditional)
{
if (null == r1mPrior || !(r1mPrior instanceof org.drip.measure.gaussian.R1MultivariateNormal) || null
== r1mConditional || !(r1mConditional instanceof org.drip.measure.gaussian.R1MultivariateNormal)
|| null == r1mUnconditional || !(r1mUnconditional instanceof
org.drip.measure.gaussian.R1MultivariateNormal))
return null;
org.drip.measure.gaussian.R1MultivariateNormal r1mnPrior =
(org.drip.measure.gaussian.R1MultivariateNormal) r1mPrior;
org.drip.measure.gaussian.R1MultivariateNormal r1mnConditional =
(org.drip.measure.gaussian.R1MultivariateNormal) r1mConditional;
org.drip.measure.gaussian.R1MultivariateNormal r1mnUnconditional =
(org.drip.measure.gaussian.R1MultivariateNormal) r1mUnconditional;
double[][] aadblPriorPrecision = r1mnPrior.covariance().precisionMatrix();
double[][] aadblConditionalPrecision = r1mnConditional.covariance().precisionMatrix();
int iNumVariate = aadblConditionalPrecision.length;
double[] adblJointMean = new double[iNumVariate];
double[][] aadblJointPrecision = new double[iNumVariate][iNumVariate];
double[][] aadblPosteriorCovariance = new double[iNumVariate][iNumVariate];
if (aadblPriorPrecision.length != iNumVariate) return null;
double[] adblPrecisionWeightedPriorMean = org.drip.numerical.linearalgebra.Matrix.Product
(aadblPriorPrecision, r1mnPrior.mean());
if (null == adblPrecisionWeightedPriorMean) return null;
double[] adblPrecisionWeightedConditionalMean = org.drip.numerical.linearalgebra.Matrix.Product
(aadblConditionalPrecision, r1mnConditional.mean());
if (null == adblPrecisionWeightedConditionalMean) return null;
for (int i = 0; i < iNumVariate; ++i) {
adblJointMean[i] = adblPrecisionWeightedPriorMean[i] + adblPrecisionWeightedConditionalMean[i];
for (int j = 0; j < iNumVariate; ++j)
aadblJointPrecision[i][j] = aadblPriorPrecision[i][j] + aadblConditionalPrecision[i][j];
}
double[][] aadblJointCovariance = org.drip.numerical.linearalgebra.Matrix.InvertUsingGaussianElimination
(aadblJointPrecision);
double[] adblJointPosteriorMean = org.drip.numerical.linearalgebra.Matrix.Product (aadblJointCovariance,
adblJointMean);
double[][] aadblUnconditionalCovariance = r1mnUnconditional.covariance().covarianceMatrix();
org.drip.measure.continuous.MultivariateMeta meta = r1mnPrior.meta();
for (int i = 0; i < iNumVariate; ++i) {
for (int j = 0; j < iNumVariate; ++j)
aadblPosteriorCovariance[i][j] = aadblJointCovariance[i][j] +
aadblUnconditionalCovariance[i][j];
}
try {
return new org.drip.measure.bayesian.R1MultivariateConvolutionMetrics (r1mPrior, r1mUnconditional,
r1mConditional, new org.drip.measure.gaussian.R1MultivariateNormal (meta,
adblJointPosteriorMean, new org.drip.measure.gaussian.Covariance (aadblJointCovariance)),
new org.drip.measure.gaussian.R1MultivariateNormal (meta, adblJointPosteriorMean, new
org.drip.measure.gaussian.Covariance (aadblPosteriorCovariance)));
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
}