norminv.mfunction 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.mfunction 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.mfunction y = beta(r,s,t) calculates the density function for
the beta distribution with parameters
$r,s$ .
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.mfunction y = betadbn(r,s,t) calculates the distribution function
for the beta distribution with parameters
$r,s$ .
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.mfunction 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.mfunction 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));
Do u think that Graphene and Fullrene fiber can be used to make Air Plane body structure the lightest and strongest.
Rafiq
Rafiq
what is differents between GO and RGO?
Mahi
what is simplest way to understand the applications of nano robots used to detect the cancer affected cell of human body.?
How this robot is carried to required site of body cell.?
what will be the carrier material and how can be detected that correct delivery of drug is done
Rafiq
Rafiq
if virus is killing to make ARTIFICIAL DNA OF GRAPHENE FOR KILLED THE VIRUS .THIS IS OUR ASSUMPTION
Anam
analytical skills graphene is prepared to kill any type viruses .
Anam
Any one who tell me about Preparation and application of Nanomaterial for drug Delivery
The nanotechnology is as new science, to scale nanometric
brayan
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Damian
Is there any normative that regulates the use of silver nanoparticles?