public double GetMle(List<int> responseVector) { LogLikelihoodFunction logLikelihoodFunction = new LogLikelihoodFunction(_modelParametersList); const double initialGuess = 0; OneDimensionalFunction firstDerivativeFunction = x => logLikelihoodFunction.LogLikelihoodFirstDerivative(responseVector, x); OneDimensionalFunction secondDerivativeFunction = x => logLikelihoodFunction.LogLikelihoodSecondDerivative(responseVector, x); //NewtonRhapsonSolver rootSolver = new NewtonRhapsonSolver(firstDerivativeFunction, secondDerivativeFunction, initialGuess); BisectionSolver rootSolver = new BisectionSolver(firstDerivativeFunction, -5, 5); double mle = rootSolver.FindRoot(); return mle; }
public void LogLikelihoodSecondDerivative_MultipleResponses_MatchesFiniteDifferenceDerivative() { double alpha1 = .3; double delta1 = .1; double chi1 = .2; ThreeParamModelParameters modelParameters1 = new ThreeParamModelParameters(alpha1, delta1, chi1); double alpha2 = .5; double delta2 = .6; double chi2 = .7; ThreeParamModelParameters modelParameters2 = new ThreeParamModelParameters(alpha2, delta2, chi2); double alpha3 = .1; double delta3 = .2; double chi3 = .4; ThreeParamModelParameters modelParameters3 = new ThreeParamModelParameters(alpha2, delta2, chi2); List<IModelParameters> modelParameterList = new List<IModelParameters>(); modelParameterList.Add(modelParameters1); modelParameterList.Add(modelParameters2); modelParameterList.Add(modelParameters3); LogLikelihoodFunction logLikelihoodFunction = new LogLikelihoodFunction(modelParameterList); double theta = .4; List<int> responseVector = new List<int>() { 1, 0, 1 }; OneDimensionalFunction derivativeFunction = x => logLikelihoodFunction.LogLikelihoodFirstDerivative(responseVector, x); FiniteDifferencer finiteDifferencer = new FiniteDifferencer(derivativeFunction); double logLikelihoodDerivative = logLikelihoodFunction.LogLikelihoodSecondDerivative(responseVector, theta); double finiteDifferenceDerivative = finiteDifferencer.ApproximateDerivative(theta); Assert.AreEqual(finiteDifferenceDerivative, logLikelihoodDerivative, Tolerance); }