# statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.pdf¶

KDEMultivariateConditional.pdf(endog_predict=None, exog_predict=None)[source]

Evaluate the probability density function.

Parameters: endog_predict (array_like, optional) – Evaluation data for the dependent variables. If unspecified, the training data is used. exog_predict (array_like, optional) – Evaluation data for the independent variables. pdf – The value of the probability density at endog_predict and exog_predict. array_like

Notes

The formula for the conditional probability density is:

$f(y|x)=\frac{f(x,y)}{f(x)}$

with

$f(x)=\prod_{s=1}^{q}h_{s}^{-1}k \left(\frac{x_{is}-x_{js}}{h_{s}}\right)$

where $$k$$ is the appropriate kernel for each variable.