11. In subjective logic the posteriori probability estimates of binary events can be represented by beta distributions . 12. After observing successes in trials, the posterior distribution for is a Beta distribution with parameters. 13. These logarithmic variances and covariance are the elements of the Fisher information matrix for the beta distribution . 14. The beta distribution is a continuous probability distribution defined over the interval 0 \ leq t \ leq 1. 15. In that special case, the prior and posterior distributions were Beta distributions and the data came from Bernoulli trials. 16. In Bayesian inference, the Beta distribution is the conjugate prior distribution for the parameter " p ". 17. This is the arcsine distribution and is a beta distribution with \ alpha = \ beta = 1 / 2. 18. Fumio Tajima demonstrated by computer simulation that the D \, statistic described above could be modeled using a beta distribution . 19. There is no general closed-form expression for the median of the beta distribution for arbitrary values of ? and ?. 20. Both are in turn special cases of the even more general " generalized beta distribution of the second kind ".
