ipoisson.m Poisson distribution — individual terms. As in the case of the
binomial distribution, we have an m-function for the individualterms and one for the cumulative case. The m-functions
ipoisson and
cpoisson use
a computational strategy similar to that used for the binomial case. Not only does this workfor large
μ , but the precision is at least as good as that for the binomial
m-functions. Experience indicates that the m-functions are good for
$\mu \le 700$ . They
breaks down at about 710, largely because of limitations of the MATLAB exponentialfunction.
For individual terms,
function y = ipoisson(mu,k) calculates the probabilities
for
$mu$ a positive integer,
k a row or column vector of nonnegative integers. The output is a row vector of the corresponding Poisson probabilities.
function y = ipoisson(mu,k)
% IPOISSON y = ipoisson(mu,k) Individual Poisson probabilities% Version of 10/15/93
% mu = mean value% k may be a row or column vector of integer values
% y = P(X = k) (a row vector of probabilities)K = max(k);
p = exp(-mu)*cumprod([1 mu*ones(1,K)]./[1 1:K]);y = p(k+1);
cpoisson.m Poisson distribution—cumulative terms.
function y = cpoisson(mu,k) , calculates
$P(X\ge k)$ , where
k may be a row or a
column vector of nonnegative integers. The output is a row vector of the corresponding probabilities.
function y = cpoisson(mu,k)
% CPOISSON y = cpoisson(mu,k) Cumulative Poisson probabilities% Version of 10/15/93
% mu = mean value mu% k may be a row or column vector of integer values
% y = P(X>= k) (a row vector of probabilities)
K = max(k);p = exp(-mu)*cumprod([1 mu*ones(1,K)]./[1 1:K]);
pc = [1 1 - cumsum(p)];
y = pc(k+1);
nbinom.m Negative binomial —
function y = nbinom(m, p, k) calculates the probability that the
m th success in a Bernoulli sequence occurs on the
k th trial.
function y = nbinom(m, p, k)
% NBINOM y = nbinom(m, p, k) Negative binomial probabilities% Version of 12/10/92
% Probability the mth success occurs on the kth trial% m a positive integer; p a probability
% k a matrix of integers greater than or equal to m% y = P(X=k) (a matrix of the same dimensions as k)
q = 1 - p;y = ((p^m)/gamma(m)).*(q.^(k - m)).*gamma(k)./gamma(k - m + 1);
gaussian.mfunction y = gaussian(m, v, t) calculates the Gaussian (Normal)
distribution function for mean value
m , variance
v , and matrix
t of values.
The result
$y=P(X\le t)$ is a matrix of the same dimensions as
t .
function y = gaussian(m,v,t)
% GAUSSIAN y = gaussian(m,v,t) Gaussian distribution function% Version of 11/18/92
% Distribution function for X ~ N(m, v)% m = mean, v = variance
% t is a matrix of evaluation points% y = P(X<=t) (a matrix of the same dimensions as t)
u = (t - m)./sqrt(2*v);if u>= 0
y = 0.5*(erf(u) + 1);else
y = 0.5*erfc(-u);end
gaussdensity.mfunction y = gaussdensity(m,v,t) calculates the
Gaussian density function
${f}_{X}\left(t\right)$ for mean value
m , variance
t , and matrix
t of values.
function y = gaussdensity(m,v,t)
% GAUSSDENSITY y = gaussdensity(m,v,t) Gaussian density% Version of 2/8/96
% m = mean, v = variance% t is a matrix of evaluation points
y = exp(-((t-m).^2)/(2*v))/sqrt(v*2*pi);
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.?
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Rafiq
Rafiq
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analytical skills graphene is prepared to kill any type viruses .
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Any one who tell me about Preparation and application of Nanomaterial for drug Delivery
The nanotechnology is as new science, to scale nanometric
brayan
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Damian
Is there any normative that regulates the use of silver nanoparticles?