Many large-scale machine learning problems involve estimating an unknown parameter $theta_{i}$ for each of many items. For example, a key problem in sponsored search is to estimate the click through rate (CTR) of each of billions of query-ad pairs. Most common methods, though, only give a point estimate of each $theta_{i}$. A posterior distribution for each $theta_{i}$ is usually more useful but harder to get. We present a simple post-processing technique that takes point estimates or scores $t_{i}$ (from any method) and estimates an approximate posterior for each $theta_{i}$. We build on the idea of calibration, a common post-processing technique that estimates $mathrm{E}left(theta_{i}!!bigm|!! t_{i}right)$. Our method, second order calibration, uses empirical Bayes methods to estimate the distribution of $theta_{i}!!bigm|!! t_{i}$ and uses the estimated distribution as an approximation to the posterior distribution of $theta_{i}$. We show that this can yield improved point estimates and useful accuracy estimates. The method scales to large problems - our motivating example is a CTR estimation problem involving tens of billions of query-ad pairs.
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