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norminv.m
function y = norminv(m,v,p) calculates the inverse
(the quantile function) of the Gaussian distribution function for mean value
m , variance
v , and
p a matrix of probabilities.
function y = norminv(m,v,p)
% NORMINV y = norminv(m,v,p) Inverse gaussian distribution% (quantile function for gaussian)
% Version of 8/17/94% m = mean, v = variance
% t is a matrix of evaluation pointsif p>= 0
u = sqrt(2)*erfinv(2*p - 1);else
u = -sqrt(2)*erfinv(1 - 2*p);end
y = sqrt(v)*u + m;
gammadbn.m
function y = gammadbn(alpha, lambda, t) calculates the
distribution function for a gamma distribution with parameters alpha, lambda.
t is a
matrix of evaluation points. The result is a matrix of the same size.
function y = gammadbn(alpha, lambda, t)
% GAMMADBN y = gammadbn(alpha, lambda, t) Gamma distribution% Version of 12/10/92
% Distribution function for X ~ gamma (alpha, lambda)% alpha, lambda are positive parameters
% t may be a matrix of positive numbers% y = P(X<= t) (a matrix of the same dimensions as t)
y = gammainc(lambda*t, alpha);
beta.m
function y = beta(r,s,t) calculates the density function for
the beta distribution with parameters
.
t is a matrix of numbers between zero and one.
The result is a matrix of the same size.
function y = beta(r,s,t)
% BETA y = beta(r,s,t) Beta density function% Version of 8/5/93
% Density function for Beta (r,s) distribution% t is a matrix of evaluation points between 0 and 1
% y is a matrix of the same dimensions as ty = (gamma(r+s)/(gamma(r)*gamma(s)))*(t.^(r-1).*(1-t).^(s-1));
betadbn.m
function y = betadbn(r,s,t) calculates the distribution function
for the beta distribution with parameters
.
t is a matrix of evaluation points. The
result is a matrix of the same size.
function y = betadbn(r,s,t)
% BETADBN y = betadbn(r,s,t) Beta distribution function% Version of 7/27/93
% Distribution function for X beta(r,s)% y = P(X<=t) (a matrix of the same dimensions as t)
y = betainc(t,r,s);
weibull.m
function y = weibull(alpha,lambda,t) calculates the density
function for the Weibull distribution with parameters alpha, lambda.
t is a matrix of
evaluation points. The result is a matrix of the same size.
function y = weibull(alpha,lambda,t)
% WEIBULL y = weibull(alpha,lambda,t) Weibull density% Version of 1/24/91
% Density function for X ~ Weibull (alpha, lambda, 0)% t is a matrix of positive evaluation points
% y is a matrix of the same dimensions as ty = alpha*lambda*(t.^(alpha - 1)).*exp(-lambda*(t.^alpha));
weibulld.m
function y = weibulld(alpha, lambda, t) calculates the
distribution function for the Weibull distribution with parameters alpha, lambda.
t is a matrix of
evaluation points. The result is a matrix of the same size.
function y = weibulld(alpha, lambda, t)
% WEIBULLD y = weibulld(alpha, lambda, t) Weibull distribution function% Version of 1/24/91
% Distribution function for X ~ Weibull (alpha, lambda, 0)% t is a matrix of positive evaluation points
% y = P(X<=t) (a matrix of the same dimensions as t)
y = 1 - exp(-lambda*(t.^alpha)); 
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