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function [a, b, XLO, XUP] = find_normpar(XLO, XUP, PLO, PUP, a0, b0);
% This function takes as inputs the bounds [XLO, XUP] in the support of the
% Normal distribution (with unknown parameters a = mean and b=standard deviation), the
% probabilities of the bounds [PLO, PUP], and the initial values for ab=[a0, b0]
% and returns the estimates of a and b (as well as XLO and XUP)
% by solving the non-linear functions in normpar(ab, XLO, XUP, PLO, PUP).
%
% Copyright (C) 1997-2012 Tao Zha
%
% This free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% It is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% If you did not received a copy of the GNU General Public License
% with this software, see <http://www.gnu.org/licenses/>.
%
if XLO >= XUP;
error('the lower bound needs to be smaller than the upper bound')
end;
if b0 <= 0;
error('the values for the standard deviation needs to be positive');
end;
disp(' ')
disp('*************** Convergence results for normal density ***************')
options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
ab_values = fsolve('normpar', [a0, b0], options, XLO, XUP, PLO, PUP);
a = ab_values(1); b = ab_values(2);
% Alternatively, it is possible to constrain the search for the values of a
% and b in the positive range (with 0 being the explicit lower bound) by
% using the lsqnonlin function (see below) instead of the fsolve. The
% tradeoff is that lsqnonlin is typically slower than fsolve.
% LB_a = 0; LB_b = 0; UB_a = Inf; UB_b = Inf;
% ab_values = lsqnonlin('normpar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
% options, XLO, XUP, PLO, PUP);
% a = ab_values(1); b = ab_values(2);
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