`scipy.stats._distribution_infrastructure:MonotonicTransformedDistribution`

source: /scipy/stats/_distribution_infrastructure.py :5447

Signature

   def     MonotonicTransformedDistribution (    X    ,  / ,   *  args    ,    g    ,    h    ,    dh    ,    logdh    =  None   ,    increasing    =  True   ,    repr_pattern    =  None   ,    str_pattern    =  None   ,   ** kwargs      )

Members

Summary

Distribution underlying a strictly monotonic function of a random variable

Extended Summary

Given a random variable $X$ ; a strictly monotonic function $g (u)$ , its inverse $h (u) = g^{- 1} (u)$ , and the derivative magnitude :math: |h'(u)| = \left| \frac{dh(u)}{du} \right|, define the distribution underlying the random variable $Y = g (X)$ .

Parameters

X : `ContinuousDistribution`: The random variable $X$ .
g, h, dh : callable: Elementwise functions representing the mathematical functions $g (u)$ , $h (u)$ , and $∣ h^{'} (u) ∣$
logdh : callable, optional: Elementwise function representing $lo g (h^{'} (u))$ . The default is lambda u: np.log(dh(u)), but providing a custom implementation may avoid over/underflow.
increasing : bool, optional: Whether the function is strictly increasing (True, default) or strictly decreasing (False).
repr_pattern : str, optional: A string pattern for determining the __repr__. The __repr__ for X will be substituted into the position where *** appears. For example: "exp(***)" for the repr of an exponentially transformed distribution The default is f"{g.__name__}(***)".
str_pattern : str, optional: A string pattern for determining __str__. The __str__ for X will be substituted into the position where *** appears. For example: "exp(***)" for the repr of an exponentially transformed distribution The default is the value repr_pattern takes.

Aliases

scipy.stats._distribution_infrastructure.MonotonicTransformedDistribution