PoissonRandomSequenceBound.java
package org.drip.sample.sequence;
import org.drip.measure.continuous.R1Univariate;
import org.drip.measure.discrete.*;
import org.drip.numerical.common.FormatUtil;
import org.drip.sequence.metrics.*;
import org.drip.sequence.random.*;
import org.drip.service.env.EnvManager;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2018 Lakshmi Krishnamurthy
* Copyright (C) 2017 Lakshmi Krishnamurthy
* Copyright (C) 2016 Lakshmi Krishnamurthy
* Copyright (C) 2015 Lakshmi Krishnamurthy
*
* This file is part of DRIP, a free-software/open-source library for buy/side financial/trading model
* libraries targeting analysts and developers
* https://lakshmidrip.github.io/DRIP/
*
* DRIP is composed of four main libraries:
*
* - DRIP Fixed Income - https://lakshmidrip.github.io/DRIP-Fixed-Income/
* - DRIP Asset Allocation - https://lakshmidrip.github.io/DRIP-Asset-Allocation/
* - DRIP Numerical Optimizer - https://lakshmidrip.github.io/DRIP-Numerical-Optimizer/
* - DRIP Statistical Learning - https://lakshmidrip.github.io/DRIP-Statistical-Learning/
*
* - DRIP Fixed Income: Library for Instrument/Trading Conventions, Treasury Futures/Options,
* Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA
* Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV
* Metrics, Stochastic Evolution and Option Pricing, Interest Rate Dynamics and Option Pricing, LMM
* Extensions/Calibrations/Greeks, Algorithmic Differentiation, and Asset Backed Models and Analytics.
*
* - DRIP Asset Allocation: Library for model libraries for MPT framework, Black Litterman Strategy
* Incorporator, Holdings Constraint, and Transaction Costs.
*
* - DRIP Numerical Optimizer: Library for Numerical Optimization and Spline Functionality.
*
* - DRIP Statistical Learning: Library for Statistical Evaluation and Machine Learning.
*
* 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.
*/
/**
* PoissonRandomSequenceBound demonstrates the Computation of the Probabilistic Bounds for a Sample Random
* Poisson Sequence.
*
* @author Lakshmi Krishnamurthy
*/
public class PoissonRandomSequenceBound {
private static final void Head (
final String strHeader)
{
System.out.println();
System.out.println ("\t|----------------------------------------------------------------------------------|");
System.out.println (strHeader);
System.out.println ("\t|----------------------------------------------------------------------------------|");
System.out.println ("\t| SIZE || <- TOLERANCES -> |");
System.out.println ("\t|----------------------------------------------------------------------------------|");
}
private static final void ChernoffStirlingBounds (
final UnivariateSequenceGenerator iidsg,
final R1Univariate dist,
final int[] aiSampleSize,
final double[] adblTolerance)
throws Exception
{
for (int iSampleSize : aiSampleSize) {
PoissonSequenceAgnosticMetrics ssamDist = (PoissonSequenceAgnosticMetrics) iidsg.sequence (
iSampleSize,
dist
);
String strDump = "\t| " + FormatUtil.FormatDouble (iSampleSize, 3, 0, 1) + " => ";
for (double dblTolerance : adblTolerance)
strDump += FormatUtil.FormatDouble (ssamDist.chernoffStirlingUpperBound (dblTolerance), 1, 9, 1.) + " | ";
System.out.println (strDump);
}
}
public static void main (
final String[] args)
throws Exception
{
EnvManager.InitEnv ("");
Poisson poissonRandom = new Poisson (10.);
PoissonDistribution poissonDistribution = new PoissonDistribution (10.);
int[] aiSampleSize = new int[] {
10, 20, 50, 100, 250
};
double[] adblTolerance = new double[] {
5., 10., 15., 20., 25.
};
Head ("\t| CHERNOFF-STIRLING BOUNDS - METRICS FROM UNDERLYING GENERATOR |");
ChernoffStirlingBounds (
poissonRandom,
poissonDistribution,
aiSampleSize,
adblTolerance
);
System.out.println ("\t|----------------------------------------------------------------------------------|");
Head ("\t| CHERNOFF-STIRLING BOUNDS - METRICS FROM EMPIRICAL DISTRIBUTION |");
ChernoffStirlingBounds (
poissonRandom,
null,
aiSampleSize,
adblTolerance
);
System.out.println ("\t|----------------------------------------------------------------------------------|");
}
}