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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

Posteriors are useful to understand the system, measure accuracy, and make better decisions. Methods like the Poisson bootstrap can help us measure the variability of $t$, but don’t give us posteriors either, particularly since good high-dimensional estimators aren’t unbiased. Figure 4 shows the results of such a test.

KDD 40
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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

Done right, strategic forecasts can provide insights to decision makers on trends, incorporate forward-looking knowledge of product plans and technology roadmaps when relevant, expose the risks and biases of relying on any one forecasting methodology, and invite input from stakeholders on the uncertainty ranges.

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The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

We use the diagnostic test results of our regression model to support the reasons why CIs should not be used in financial data analyses. The probability of an event should be measured empirically by repeating similar experiments ad nauseam —either in reality or hypothetically. Frequently used interpretation of probability.