Controlled Discovery and Localization of Signals via Bayesian Linear Programming

TL;DR

The paper introduces Bayesian Linear Programming (BLiP), a method that improves the detection and localization of signals in high-dimensional data by maximizing power while controlling false positives, applicable across various scientific fields.

Contribution

BLiP provides a computationally efficient approach to derive credible regions from Bayesian posteriors, nearly maximizing expected power and controlling false positives, outperforming existing methods.

Findings

Increased power by 30-120% in genetic and astronomical data analyses.

Efficiently wraps around existing Bayesian models and algorithms.

Applicable to diverse signal detection problems.

Abstract

Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locations of causal variants. So the statistical task is to output as many disjoint regions containing a signal as possible, each as small as possible, while controlling false positives. Similar problems arise in any application where signals cannot be perfectly localized, such as locating stars in astronomical surveys and changepoint detection in sequential data. Common Bayesian approaches to these problems involve computing a posterior distribution over signal locations. However, existing procedures to translate these posteriors into actual credible regions for the signals fail to capture all the information in the posterior, leading to lower power and (sometimes) inflated false discoveries. With this motivation, we introduce Bayesian Linear Programming (BLiP). Given a posterior distribution over signals, BLiP outputs credible regions for signals which verifiably nearly maximize expected power while controlling false positives. BLiP overcomes an extremely high-dimensional and nonconvex problem to verifiably nearly maximize expected power while controlling false positives. BLiP is very computationally efficient compared to the cost of computing the posterior and can wrap around nearly any Bayesian model and algorithm. Applying BLiP to existing state-of-the-art analyses of UK Biobank data (for genetic fine-mapping) and the Sloan Digital Sky Survey (for astronomical point source detection) increased power by 30-120% in just a few minutes of additional computation. BLiP is implemented in pyblip (Python) and blipr (R).