Integration method to map model scores to conversion rates from example data

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.

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Why software projects take longer than you think – a statistical model

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.

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A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

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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.

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