| 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 ".
|
