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
andexog_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.
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.pdf.html