gemseo / uncertainty / statistics / tolerance_interval

lognormal module

Computation of tolerance intervals from a data-fitted log-normal distribution.

class gemseo.uncertainty.statistics.tolerance_interval.lognormal.LogNormalToleranceInterval(size, mean, std, location)[source]

Bases: NormalToleranceInterval

Computation of tolerance intervals from a data-fitted log-normal distribution.

The formulae come from the R library tolerance [1].

Parameters:
  • size (int) – The number of samples.

  • mean (float) – The estimation of the mean of the natural logarithm of a log-normal distributed random variable.

  • std (float) – The estimation of the standard deviation of the natural logarithm of a log-normal distributed random variable.

  • location (float) – The estimation of the location of the log-normal distributed.

compute(coverage, confidence=0.95, side=ToleranceIntervalSide.BOTH)[source]

Compute a tolerance interval.

Parameters:
  • coverage (float) – A minimum percentage of belonging to the TI.

  • confidence (float) –

    A level of confidence in [0,1].

    By default it is set to 0.95.

  • side (ToleranceIntervalSide) –

    The type of the tolerance interval characterized by its sides of interest, either a lower-sided tolerance interval \([a, +\infty[\), an upper-sided tolerance interval \(]-\infty, b]\), or a two-sided tolerance interval \([c, d]\).

    By default it is set to BOTH.

Returns:

The tolerance bounds.

Return type:

tuple[numpy.ndarray, numpy.ndarray]

docstring_processor(parent_doc: Optional[str], child_func: Callable) None: Callable[[Optional[str], Callable], str] = <docstring_inheritance.processors.google.GoogleDocstringProcessor object>
Parameters:
Return type:

None