gemseo / uncertainty / distributions / scipy

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normal module

The SciPy-based normal distribution.

class gemseo.uncertainty.distributions.scipy.normal.SPNormalDistribution(variable='x', mu=0.0, sigma=1.0, dimension=1)

Bases: SPDistribution

The SciPy-based normal distribution.

Examples

>>> from gemseo.uncertainty.distributions.scipy.normal import (
...     SPNormalDistribution,
... )
>>> distribution = SPNormalDistribution("x", -1, 2)
>>> print(distribution)
norm(mu=-1, sigma=2)

Parameters:

• variable (str) –

  The name of the random variable.

  By default it is set to “x”.

• mu (float) –

  The mean of the normal random variable.

  By default it is set to 0.0.

• sigma (float) –

  The standard deviation of the normal random variable.

  By default it is set to 1.0.

• dimension (int) –

  The dimension of the random variable. If greater than 1, the probability distribution is applied to all components of the random variable under the hypothesis that these components are stochastically independent. To be removed in a future version; use a ComposedDistribution instead.

  By default it is set to 1.

dimension: int

The number of dimensions of the random variable.

distribution: type

The probability distribution of the random variable.

distribution_name: str

The name of the probability distribution.

marginals: list[type]

The marginal distributions of the components of the random variable.

math_lower_bound: ndarray

The mathematical lower bound of the random variable.

math_upper_bound: ndarray

The mathematical upper bound of the random variable.

num_lower_bound: ndarray

The numerical lower bound of the random variable.

num_upper_bound: ndarray

The numerical upper bound of the random variable.

parameters: tuple[Any] | dict[str, Any]

The parameters of the probability distribution.

standard_parameters: dict[str, str] | None

The standard representation of the parameters of the distribution, used for its string representation.

transformation: str

The transformation applied to the random variable, e.g. ‘sin(x)’.

variable_name: str

The name of the random variable.

Examples using SPNormalDistribution

Probability distributions based on SciPy

Probability distributions based on SciPy