function [a, b, XLO, XUP] = find_betapar(XLO, XUP, PLO, PUP, a0, b0);

% This function takes as inputs the bounds [XLO, XUP] in the support of the
% Beta distribution (with unknown parameters a and b), 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 betapar(ab, XLO, XUP, PLO, PUP).

%-----------------------------------------------------------------------------------
%------------------------------- Beta distribution --------------------------------%
%--- p(x) = ( Gamma(a+b)/(Gamma(a)*Gamma(b)) ) x^(a-1) (1-x)^(b-1)
%         = (1/Beta(a, b))x^(a-1) (1-x)^(b-1)  for a>0 and b>0, and
%         0<=x<=1.
%--- E(x) = a/(a+b);  var(x) = a*b/( (a+b)^2*(a+b+1) );
%--- The density is finite if a,b>=1.
%--- Noninformative density: (1) a=b=1; (2) a=b=0.5; or (3) a=b=0.
%-----------------------------------------------------------------------------------
%
% 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')
elseif XLO<=0 or XUP >=1;
    error('the support for Beta distribution needs to be between 0 and 1');
end;

if a0 <= 0 || b0 <= 0;
    error('the values for a and b need to be positive');
end;

disp(' ')
disp('*************** Convergence results for beta density ***************')
options = optimset('Display', 'on','TolFun', 1.0e-10, 'TolX', 1.0e-10);
ab_values = fsolve('betapar', [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('betapar', [a0, b0], [LB_a, LB_b], [UB_a, UB_b],...
%     options, XLO, XUP, PLO, PUP);
% a = ab_values(1); b = ab_values(2);
