Altaxo. |
[Missing <summary> documentation for "N:Altaxo.Calc.Distributions"]
Classes| Class | Description | |
|---|---|---|
 | Bernoulli | Discrete Univariate Bernoulli distribution. The Bernoulli distribution is a distribution over bits. The parameter p specifies the probability that a 1 is generated. Wikipedia - Bernoulli distribution. |
| Beta | Continuous Univariate Beta distribution. For details about this distribution, see Wikipedia - Beta distribution. |
| BetaBinomial | Discrete Univariate Beta-Binomial distribution. The beta-binomial distribution is a family of discrete probability distributions on a finite support of non-negative integers arising when the probability of success in each of a fixed or known number of Bernoulli trials is either unknown or random. The beta-binomial distribution is the binomial distribution in which the probability of success at each of n trials is not fixed but randomly drawn from a beta distribution. It is frequently used in Bayesian statistics, empirical Bayes methods and classical statistics to capture overdispersion in binomial type distributed data. Wikipedia - Beta-Binomial distribution. |
| BetaScaled | |
| Binomial | Discrete Univariate Binomial distribution. For details about this distribution, see Wikipedia - Binomial distribution. |
| Burr | |
| Categorical | Discrete Univariate Categorical distribution. For details about this distribution, see Wikipedia - Categorical distribution. This distribution is sometimes called the Discrete distribution. |
| Cauchy | Continuous Univariate Cauchy distribution. The Cauchy distribution is a symmetric continuous probability distribution. For details about this distribution, see Wikipedia - Cauchy distribution. |
| Chi | Continuous Univariate Chi distribution. This distribution is a continuous probability distribution. The distribution usually arises when a k-dimensional vector's orthogonal components are independent and each follow a standard normal distribution. The length of the vector will then have a chi distribution. Wikipedia - Chi distribution. |
| ChiSquared | Continuous Univariate Chi-Squared distribution. This distribution is a sum of the squares of k independent standard normal random variables. Wikipedia - ChiSquare distribution. |
| ContinuousUniform | Continuous Univariate Uniform distribution. The continuous uniform distribution is a distribution over real numbers. For details about this distribution, see Wikipedia - Continuous uniform distribution. |
| ConwayMaxwellPoisson |
Discrete Univariate Conway-Maxwell-Poisson distribution.
The Conway-Maxwell-Poisson distribution is a generalization of the Poisson, Geometric and Bernoulli distributions. It is parameterized by two real numbers "lambda" and "nu". For
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| Dirichlet | Multivariate Dirichlet distribution. For details about this distribution, see Wikipedia - Dirichlet distribution. |
| DiscreteUniform | Discrete Univariate Uniform distribution. The discrete uniform distribution is a distribution over integers. The distribution is parameterized by a lower and upper bound (both inclusive). Wikipedia - Discrete uniform distribution. |
| Erlang | Continuous Univariate Erlang distribution. This distribution is a continuous probability distribution with wide applicability primarily due to its relation to the exponential and Gamma distributions. Wikipedia - Erlang distribution. |
| Exponential | Continuous Univariate Exponential distribution. The exponential distribution is a distribution over the real numbers parameterized by one non-negative parameter. Wikipedia - exponential distribution. |
| FisherSnedecor | Continuous Univariate F-distribution, also known as Fisher-Snedecor distribution. For details about this distribution, see Wikipedia - FisherSnedecor distribution. |
| Gamma | Continuous Univariate Gamma distribution. For details about this distribution, see Wikipedia - Gamma distribution. |
| Geometric | Discrete Univariate Geometric distribution. The Geometric distribution is a distribution over positive integers parameterized by one positive real number. This implementation of the Geometric distribution will never generate 0's. Wikipedia - geometric distribution. |
| Hypergeometric | Discrete Univariate Hypergeometric distribution. This distribution is a discrete probability distribution that describes the number of successes in a sequence of n draws from a finite population without replacement, just as the binomial distribution describes the number of successes for draws with replacement Wikipedia - Hypergeometric distribution. |
| InverseGamma | Continuous Univariate Inverse Gamma distribution. The inverse Gamma distribution is a distribution over the positive real numbers parameterized by two positive parameters. Wikipedia - InverseGamma distribution. |
| InverseGaussian | |
| InverseWishart | Multivariate Inverse Wishart distribution. This distribution is parameterized by the degrees of freedom nu and the scale matrix S. The inverse Wishart distribution is the conjugate prior for the covariance matrix of a multivariate normal distribution. Wikipedia - Inverse-Wishart distribution. |
| Laplace | Continuous Univariate Laplace distribution. The Laplace distribution is a distribution over the real numbers parameterized by a mean and scale parameter. The PDF is: p(x) = \frac{1}{2 * scale} \exp{- |x - mean| / scale}. Wikipedia - Laplace distribution. |
| Logistic | Continuous Univariate Logistic distribution. For details about this distribution, see Wikipedia - Logistic distribution. |
| LogNormal | Continuous Univariate Log-Normal distribution. For details about this distribution, see Wikipedia - Log-Normal distribution. |
| MatrixNormal | Multivariate Matrix-valued Normal distributions. The distribution is parameterized by a mean matrix (M), a covariance matrix for the rows (V) and a covariance matrix for the columns (K). If the dimension of M is d-by-m then V is d-by-d and K is m-by-m. Wikipedia - MatrixNormal distribution. |
| Multinomial | Multivariate Multinomial distribution. For details about this distribution, see Wikipedia - Multinomial distribution. |
| NegativeBinomial | Discrete Univariate Negative Binomial distribution. The negative binomial is a distribution over the natural numbers with two parameters r, p. For the special case that r is an integer one can interpret the distribution as the number of failures before the r'th success when the probability of success is p. Wikipedia - NegativeBinomial distribution. |
| Normal | Continuous Univariate Normal distribution, also known as Gaussian distribution. For details about this distribution, see Wikipedia - Normal distribution. |
| NormalGamma |
Multivariate Normal-Gamma Distribution.
The NormalGamma distribution is the conjugate prior distribution for the Normal distribution. It specifies a prior over the mean and precision of the Normal distribution. It is parameterized by four numbers: the mean location, the mean scale, the precision shape and the precision inverse scale. The distribution NG(mu, tau | mloc,mscale,psscale,pinvscale) = Normal(mu | mloc, 1/(mscale*tau)) * Gamma(tau | psscale,pinvscale). The following degenerate cases are special: when the precision is known, the precision shape will encode the value of the precision while the precision inverse scale is positive infinity. When the mean is known, the mean location will encode the value of the mean while the scale will be positive infinity. A completely degenerate NormalGamma distribution with known mean and precision is possible as well. Wikipedia - Normal-Gamma distribution. |
| Pareto | Continuous Univariate Pareto distribution. The Pareto distribution is a power law probability distribution that coincides with social, scientific, geophysical, actuarial, and many other types of observable phenomena. For details about this distribution, see Wikipedia - Pareto distribution. |
| Poisson | Discrete Univariate Poisson distribution. |
| Rayleigh | Continuous Univariate Rayleigh distribution. The Rayleigh distribution (pronounced /ˈreɪli/) is a continuous probability distribution. As an example of how it arises, the wind speed will have a Rayleigh distribution if the components of the two-dimensional wind velocity vector are uncorrelated and normally distributed with equal variance. For details about this distribution, see Wikipedia - Rayleigh distribution. |
| SkewedGeneralizedError | Continuous Univariate Skewed Generalized Error Distribution (SGED). Implements the univariate Skewed Generalized Error Distribution. For details about this distribution, see Wikipedia - Generalized Error Distribution. It includes Laplace, Normal and Student-t distributions. This is the SkewedGeneralizedT distribution with q=Inf. |
| SkewedGeneralizedT | Continuous Univariate Skewed Generalized T-distribution. Implements the univariate Skewed Generalized t-distribution. For details about this distribution, see Wikipedia - Skewed generalized t-distribution. The skewed generalized t-distribution contains many different distributions within it as special cases based on the parameterization chosen. |
| Stable | Continuous Univariate Stable distribution. A random variable is said to be stable (or to have a stable distribution) if it has the property that a linear combination of two independent copies of the variable has the same distribution, up to location and scale parameters. For details about this distribution, see Wikipedia - Stable distribution. |
| StudentT | Continuous Univariate Student's T-distribution. Implements the univariate Student t-distribution. For details about this distribution, see Wikipedia - Student's t-distribution. |
| Triangular | Triangular distribution. For details, see Wikipedia - Triangular distribution. |
| TruncatedPareto | |
| Weibull | Continuous Univariate Weibull distribution. For details about this distribution, see Wikipedia - Weibull distribution. |
| Wishart | Multivariate Wishart distribution. This distribution is parameterized by the degrees of freedom nu and the scale matrix S. The Wishart distribution is the conjugate prior for the precision (inverse covariance) matrix of the multivariate normal distribution. Wikipedia - Wishart distribution. |
| Zipf | Discrete Univariate Zipf distribution. Zipf's law, an empirical law formulated using mathematical statistics, refers to the fact that many types of data studied in the physical and social sciences can be approximated with a Zipfian distribution, one of a family of related discrete power law probability distributions. For details about this distribution, see Wikipedia - Zipf distribution. |
Structures| Structure | Description | |
|---|---|---|
 | MeanPrecisionPair | This structure represents the type over which the NormalGamma distribution is defined. |
Interfaces| Interface | Description | |
|---|---|---|
 | IContinuousDistribution | Continuous Univariate Probability Distribution. |
| IDiscreteDistribution | Discrete Univariate Probability Distribution. |
| IDistribution | Probability Distribution. |
| IUnivariateDistribution | Univariate Probability Distribution. |