nlogL = betalike (params, x) returns the negative log likelihood of the data in x corresponding to the Beta distribution with (1) shape parameter $\alpha$ and (2) shape parameter $\beta$ given in the two-element vector params. Both parameters must be positive real numbers and the data in the range $[0,1]$. Out of range parameters or data return NaN.

[nlogL, avar] = betalike (params, x) returns the inverse of Fisher’s information matrix, avar. If the input parameter values in params are the maximum likelihood estimates, the diagonal elements of params are their asymptotic variances.

[…] = betalike (params, x, freq) accepts a frequency vector, freq, of the same size as x. freq must contain non-negative integer frequencies for the corresponding elements in x. By default, or if left empty, freq = ones (size (x)).

The Beta distribution is defined on the open interval $(0,1)$. However, betafit can also compute the unbounded beta likelihood function for data that include exact zeros or ones. In such cases, zeros and ones are treated as if they were values that have been left-censored at sqrt (realmin) or right-censored at 1 - eps/2, respectively.

Further information about the Beta distribution can be found at https://en.wikipedia.org/wiki/Beta_distribution

See also: betacdf, betainv, betapdf, betarnd, betafit, betastat

Source Code: betalike