New answers tagged conditioned
I think the real question is why are you using SmoothKernelDistribution at all here, and what do you hope to achieve by doing so? In particular, why do you think that, given sample data: inferring a smoothed pdf from the sample data (and using the default settings for bandwidth choice and kernel choice, when there are an infinite number of possibilities ...
As a work-around : Expectation[x \[Conditioned] x > 30, x \[Distributed] MarginalDistribution[speedDistr, 2]] (* 34.8138 *)
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