CombinatorialEstimate.java
package org.drip.specialfunction.beta;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 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>CombinatorialEstimate</i> implements the Combinatorial Function Estimate using Beta-based Schemes. The
* References are:
*
* <br><br>
* <ul>
* <li>
* Abramowitz, M., and I. A. Stegun (2007): <i>Handbook of Mathematics Functions</i> <b>Dover Book
* on Mathematics</b>
* </li>
* <li>
* Davis, P. J. (1959): Leonhard Euler's Integral: A Historical Profile of the Gamma Function
* <i>American Mathematical Monthly</i> <b>66 (10)</b> 849-869
* </li>
* <li>
* Whitaker, E. T., and G. N. Watson (1996): <i>A Course on Modern Analysis</i> <b>Cambridge
* University Press</b> New York
* </li>
* <li>
* Wikipedia (2019): Beta Function https://en.wikipedia.org/wiki/Beta_function
* </li>
* <li>
* Wikipedia (2019): Gamma Function https://en.wikipedia.org/wiki/Gamma_function
* </li>
* </ul>
*
* <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/FunctionAnalysisLibrary.md">Function Analysis Library</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/specialfunction/README.md">Special Function Implementation Analysis</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/specialfunction/beta/README.md">Estimation Techniques for Beta Function</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class CombinatorialEstimate
{
/**
* Estimate the Binomial Coefficient Using the Beta Function
*
* @param n n
* @param k k
* @param betaEstimator The Beta Function Estimator
*
* @return Binomial Coefficient Using the Beta Function
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public static final double BetaBinomial (
final double n,
final double k,
final org.drip.function.definition.R2ToR1 betaEstimator)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (n) || 0. >= n ||
!org.drip.numerical.common.NumberUtil.IsValid (k) || 0. >= k ||
n < k ||
null == betaEstimator)
{
throw new java.lang.Exception ("CombinatorialEstimate::BetaBinomial => Invalid Inputs");
}
return 1. / (
(n + 1.) * betaEstimator.evaluate (
n - k + 1.,
k + 1.
)
);
}
/**
* Estimate the Binomial Coefficient Using a Continuous Interpolation Function
*
* @param n n
* @param k k
* @param gammaEstimator The Gamma Function Estimator
*
* @return Binomial Coefficient Using a Continuous Interpolation Function
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public static final double GammaBinomial (
final double n,
final double k,
final org.drip.function.definition.R1ToR1 gammaEstimator)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (n) || 0. >= n ||
!org.drip.numerical.common.NumberUtil.IsValid (k) || 0. >= k ||
n < k ||
null == gammaEstimator)
{
throw new java.lang.Exception ("CombinatorialEstimate::GammaBinomial => Invalid Inputs");
}
double gammaBinomial = (1 == n % 2 ? -1. : 1.) * gammaEstimator.evaluate (n + 1.) *
java.lang.Math.sin (java.lang.Math.PI * k) / java.lang.Math.PI;
for (int i = 0; i <= n; ++i)
{
gammaBinomial = gammaBinomial / (k - i);
}
return gammaBinomial;
}
/**
* Compute the Cumulative Binomial Distribution Function for the specified n, k, and p
*
* @param n n
* @param k k
* @param p p
* @param incompleteRegularizedEstimator The Incomplete Regularized Beta Estimator
*
* @return The Cumulative Binomial Distribution Function Value
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public static final double CumulativeBinomialDistribution (
final double n,
final double k,
final double p,
final org.drip.specialfunction.beta.IncompleteRegularizedEstimator incompleteRegularizedEstimator)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (n) || 0. >= n ||
!org.drip.numerical.common.NumberUtil.IsValid (k) || 0. >= k ||
n < k ||
!org.drip.numerical.common.NumberUtil.IsValid (p) || 0. > p || 1. < p ||
null == incompleteRegularizedEstimator)
{
throw new java.lang.Exception
("CombinatorialEstimate::CumulativeBinomialDistribution => Invalid Inputs");
}
return incompleteRegularizedEstimator.evaluate (
1. - p,
n - k,
k + 1.
);
}
}