Hey, love this package, and want to make sure I'm understanding how it works.
For a bivariate kernel density, if you sum down a column, then multiply by the x step distance, does that equal the marginal probability of the x variable taking that sliver of range? For example:
$chf,eur \in R^n$
B1 = kde((chf,eur))
x_step = B1.x[2]-B1.x[2]
marginal_probability_1 = sum(B1.density[:,1] .* x_step)
Is the following statement true?
$$ \text{marginal probability 1}= P(X[1] \leq X \leq X[2]) $$
Or should I be using y_step to calculate marginal probability 1?
Sorry for the (maybe too simplistic) question. I appreciate any help you're willing to provide.
Hey, love this package, and want to make sure I'm understanding how it works.
For a bivariate kernel density, if you sum down a column, then multiply by the x step distance, does that equal the marginal probability of the x variable taking that sliver of range? For example:
Is the following statement true?
Or should I be using y_step to calculate
marginal probability 1?Sorry for the (maybe too simplistic) question. I appreciate any help you're willing to provide.