To do the optimization, we use the mle function in Statistics and Machine Learning Toolbox to perform maximum-likelihood estimation, specifying the negative log-likelihood function and the parameter bound constraints as inputs. For this problem the estimator is the Maximum Likelihood Estimator (MLE) and. Estimation is based on the Black-Scholes/Merton model, where () is a function of (). outcomeCounts ( 2 )/ nTrials ( cc ) end for cc = 1 : length ( stimCounts ) h = scatter ( stim ( cc ), pCorrect ( cc ), 100, 'o', 'MarkerEdgeColor', 'MarkerFaceColor' . Template for parameter estimation with Matlab Optimization Toolbox. 1) You need to have Matlabs statistics and optimization toolboxes. See Table 2-4, Large-Scale Problem Coverage and. In the underdetermined case, the medium-scale algorithm is used instead. outcomeCounts ) pCorrect ( cc ) = stimCounts ( cc ). This zip file contains 5 functions: the pdf, cdf, log-likelihood, inverse cdf. The toolbox uses the maximum likelihood method to estimate model parameters from the data. The large-scale method for lsqcurvefit does not solve underdetermined systems it requires that the number of equations, i.e., row dimension of, be at least as great as the number of variables. %% Plot of trial locations with maximum likelihood fit figure clf hold on stimCounts = qpCounts ( qpData ( trialData ), nOutcomes ) stim = stimFine = linspace ( - 40, 0, 100 ) ' plotProportionsFit = qpPFWeibull ( stimFine, psiParamsFit ) for cc = 1 : length ( stimCounts ) nTrials ( cc ) = sum ( stimCounts ( cc ).
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