gemseo / uncertainty / statistics / tolerance_interval

normal module

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

class gemseo.uncertainty.statistics.tolerance_interval.normal.NormalToleranceInterval(size, mean, std)[source]

Bases: gemseo.uncertainty.statistics.tolerance_interval.distribution.ToleranceInterval

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

The formulae come from the R library tolerance 1.


Derek S. Young, tolerance: An R Package for Estimating Tolerance Intervals, Journal of Statistical Software, 36(5), 2010

  • size (int) – The number of samples.

  • mean (float) – The estimation of the mean of the normal distribution.

  • std (float) – The estimation of the standard deviation of the normal distribution.

Return type


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

Compute a tolerance interval.

  • 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 (gemseo.uncertainty.statistics.tolerance_interval.distribution.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.


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] = < object>
  • parent_doc (Optional[str]) –

  • child_func (Callable) –

Return type