185 lines
6.3 KiB
Matlab
185 lines
6.3 KiB
Matlab
function [fval,cost_flag,ys,trend_coeff] = DsgeLikelihood(xparam1,gend,data)
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% stephane.adjemian@cepremap.cnrs.fr [09-07-2004]
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%
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% Adapted from mj_optmumlik.m
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global bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_
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global dr1_test_
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fval = [];
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ys = [];
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trend_coeff = [];
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cost_flag = 1;
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nobs = size(options_.varobs,1);
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
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k = find(xparam1 < bayestopt_.lb);
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fval = bayestopt_.penalty*min(1e3,exp(sum(bayestopt_.lb(k)-xparam1(k))));
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cost_flag = 0;
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return;
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end
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if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
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k = find(xparam1 > bayestopt_.ub);
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fval = bayestopt_.penalty*min(1e3,exp(sum(xparam1(k)-bayestopt_.ub(k))));
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cost_flag = 0;
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return;
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end
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Q = M_.Sigma_e;
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for i=1:estim_params_.nvx
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k =estim_params_.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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end
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offset = estim_params_.nvx;
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if estim_params_.nvn
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H = zeros(nobs,nobs);
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for i=1:estim_params_.nvn
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k = estim_params_.var_endo(i,1);
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H(k,k) = xparam1(i+offset)*xparam1(i+offset);
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end
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offset = offset+estim_params_.nvn;
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end
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if estim_params_.ncx
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for i=1:estim_params_.ncx
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k1 =estim_params_.corrx(i,1);
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k2 =estim_params_.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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[CholQ,testQ] = chol(Q);
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if testQ %% The variance-covariance matrix of the structural innovations is not definite positive.
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%% We have to compute the eigenvalues of this matrix in order to build the penalty.
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a = diag(eig(Q));
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(a<=0))));
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cost_flag = 0;
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return
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end
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offset = offset+estim_params_.ncx;
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end
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if estim_params_.ncn
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for i=1:estim_params_.ncn
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k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
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k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(k1,k2);
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end
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[CholH,testH] = chol(H);
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if testH
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a = diag(eig(H));
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if nobs == estim_params_.nvn
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(a<=0))));
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cost_flag = 0;
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return
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else
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if sum(abs(a)<crit) == nobs-estim_params_.nvn
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if any(a<0)
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(a<0))));
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cost_flag = 0;
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return
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else
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% All is fine, there's nothing to do here...
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end
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else
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fval = bayestopt_.penalty*min(1e3,exp(sum(-a(a<=0))));
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cost_flag = 0;
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return
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end
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end
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end
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offset = offset+estim_params_.ncn;
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end
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for i=1:estim_params_.np
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M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);
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end
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M_.Sigma_e = Q;
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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[T,R,SteadyState] = dynare_resolve;
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if dr1_test_(1) == 1
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fval = bayestopt_.penalty*min(1e3,exp(dr1_test_(2)));
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cost_flag = 0;
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return
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elseif dr1_test_(1) == 2
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fval = bayestopt_.penalty*min(1e3,exp(dr1_test_(2)));
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cost_flag = 0;
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return
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elseif dr1_test_(1) == 3
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fval = bayestopt_.penalty*min(1e3,exp(dr1_test_(2)));
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cost_flag = 0;
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return
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end
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if options_.loglinear == 1
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constant = log(SteadyState(bayestopt_.mfys));
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else
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constant = SteadyState(bayestopt_.mfys);
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end
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if bayestopt_.with_trend == 1
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trend_coeff = zeros(nobs,1);
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for i=1:nobs
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trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
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end
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trend = constant*ones(1,gend)+trend_coeff*(1:gend);
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else
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trend = constant*ones(1,gend);
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end
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start = options_.presample+1;
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np = size(T,1);
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mf = bayestopt_.mf;
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%------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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%------------------------------------------------------------------------------
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*transpose(R));
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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Pstar = zeros(np,np);
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ivs = bayestopt_.i_T_var_stable;
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Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
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transpose(R(ivs,:)));
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Pinf = bayestopt_.Pinf;
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end
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%------------------------------------------------------------------------------
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% 4. Likelihood evaluation
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%------------------------------------------------------------------------------
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if estim_params_.nvn
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if options_.kalman_algo == 1
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LIK = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,data,trend,start);
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if isinf(LIK) & ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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elseif isinf(LIK) & estim_params_.ncn
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LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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if ~estim_params_.ncn %% The univariate approach considered here doesn't
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%% apply when H has some off-diagonal elements.
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LIK = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,data,trend,start);
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else
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LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,data,trend,start);
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end
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end
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else
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if options_.kalman_algo == 1
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LIK = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);
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if isinf(LIK)
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LIK = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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elseif options_.kalman_algo == 3
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LIK = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,data,trend,start);
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end
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end
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if imag(LIK) ~= 0
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likelihood = bayestopt_.penalty;
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else
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likelihood = LIK;
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end
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% ------------------------------------------------------------------------------
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% Adds prior if necessary
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% ------------------------------------------------------------------------------
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lnprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
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fval = (likelihood-lnprior);
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