statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.pdf¶
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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.
Returns: pdf – The value of the probability density at endog_predict and exog_predict.
Return type: 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.