This note addresses the typical applied problem of estimating from data how a target “conversion rate” function varies with some available scalar score function — e.g., estimating conversion rates from some marketing campaign as a function of a targeting model score. The idea centers around estimating the integral of the rate function; differentiating this gives the rate function.Read Full Story
Anyone who built software for a while knows that estimating how long something is going to take is hard.
It’s hard to come up with an unbiased estimate of how long something will take, when fundamentally the work in itself is about solving something.
One pet theory I’ve had for a really long time, is that some of this is really just a statistical artifact.
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain.
Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution, such as the mean or mode.
Probabilistic inference involves estimating an expected value or density using a probabilistic model.
Often, directly inferring values is not tractable with probabilistic models, and instead, approximation methods must be used.