|
Class Summary |
| BetaDistribution |
BetaDistribution distribution is the posterior distribution of the parameter p of a binomial distribution
after observing α − 1 independent events with probability p and
β − 1 with probability 1 − p,
if the prior distribution of p is uniform. |
| ChiSquareDistribution |
Chi-square distribution is the distribution of
the sum of the squares of a set of statistically independent standard Gaussian random variables. |
| EmpiricalDistribution |
An empirical cumulative probability distribution function
is a cumulative probability distribution function that
assigns probability 1/n at each of the n numbers in a sample. |
| ExponentialDistribution |
An exponential distribution describes the times between events in a Poisson process,
a process in which events occur continuously and independently at a constant average rate. |
| FDistribution |
FDistribution distribution is the distribution of the ratio of two independent chi-squared variates. |
| GammaDistribution |
GammaDistribution distribution, when k is an integer, is the distribution of
the sum of k independent exponentially distributed random variables,
each of which has a mean of θ (which is equivalent to a rate parameter of θ−1). |
| NormalDistribution |
The NormalDistribution distribution has its density of a Gaussian function. |
| RayleighDistribution |
The L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and
have RayleighDistribution distributions. |
| TDistribution |
Student's t distribution is the probability distribution of t, where
x̄ - μ
t = ------------
s / sqrt(N)
x̄ is the sample mean;
μ is the population mean;
s is the square root of the sample variance;
N is the sample size;
The importance of the Student's distribution is
when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data. |
| WeibullDistribution |
The WeibullDistribution distribution interpolates between the exponential distribution (k = 1) and the Rayleigh distribution (k = 2),
where k is the shape parameter. |