Beta probability density function   Y = betapdf(X,A,B)   Y =
betapdf(X,A,B) computes the beta pdf at each of the values in X
using the corresponding parameters in A and B. X, A, and B can
be vectors, matrices, or multidimensional arrays that all have
the same size. A scalar input is expanded to a constant array
with the same dimensions of the other inputs. The parameters in
A and B must all be positive, and the values in X must lie on
the interval [0, 1].  The beta probability density function for
a given value x and given pair of parameters a and b is   where
B( · ) is the Beta function. The indicator function I(0,1)(x)
ensures that only values of x in the range (0 1) have nonzero
probability. The uniform distribution on (0 1) is a degenerate
case of the beta pdf where a = 1 and b = 1.  A likelihood
function is the pdf viewed as a function of the parameters.
Maximum likelihood estimators (MLEs) are the values of the
parameters that maximize the likelihood function for a fixed
value of x.   a = [0.5 1; 2 4] a