Interface  Description 

ProbabilityDistribution 
A univariate probability distribution completely characterizes a random variable by stipulating
the probability of each value of a random variable (when the variable is discrete), or the
probability of the value falling within a particular interval (when the variable is continuous).

Class  Description 

BetaDistribution 
The beta 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.

BinomialDistribution 
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments,
each of which yields success with probability p.

ChiSquareDistribution 
The Chisquare 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 
The 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 
The F distribution is the distribution of the ratio of two independent chisquared variates.

GammaDistribution 
This gamma 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}).

LogNormalDistribution 
A lognormal distribution is a probability distribution of a random variable whose logarithm is normally distributed.

NormalDistribution 
The Normal distribution has its density a Gaussian function.

PoissonDistribution 
The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution
that expresses the probability of a given number of events occurring in a fixed interval of time
and/or space if these events occur with a known average rate and independently of the time since
the last event.

RayleighDistribution 
The L2 norm of (x1, x2), where x_{i}'s are normal, uncorrelated, equal variance and
have the Rayleigh distributions.

TDistribution 
The Student t distribution is the probability distribution of t, where
\[
t = \frac{\bar{x}  \mu}{s / \sqrt N}
\]
\(\bar{x}\) 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.

TriangularDistribution 
The triangular distribution is a continuous probability distribution with lower limit a, upper
limit b and mode c, where a < b and a ≤ c ≤ b.

TruncatedNormalDistribution 
The truncated Normal distribution is the probability distribution of a normally distributed
random variable whose value is either bounded below or above (or both).

WeibullDistribution 
The Weibull distribution interpolates between the exponential distribution k = 1 and the
Rayleigh distribution (k = 2),
where k is the shape parameter.

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