An easy-to-use technique could assist everyone from economists to sports analysts.
Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific technique called Bayesian inference.
Bayesian inference allows these scientists to effectively estimate some unknown parameter — like the winner of an election — from data such as poll results. But Bayesian inference can be slow, sometimes consuming weeks or even months of computation time or requiring a researcher to spend hours deriving tedious equations by hand.
Researchers from MIT and elsewhere have introduced an optimization technique that speeds things up without requiring a scientist to do a lot of additional work. Their method can achieve more accurate results faster than another popular approach for accelerating Bayesian inference.
Using this new automated technique, a scientist could simply input their model and then the optimization method does all the calculations under the hood to provide an approximation of some unknown parameter. The method also offers reliable uncertainty estimates that can help a researcher understand when to trust its predictions.
This versatile technique could be applied to a wide array of scientific quandaries that incorporate Bayesian inference. For instance, it could be used by economists studying the impact of microcredit loans in developing nations or sports analysts using a model to rank top tennis players.
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