Prediction of particle type from measurements of particle location: A physicist's approach to Bayesian classification

TL;DR

This paper explores Bayesian classification for predicting particle types from location data, comparing it with other methods and discussing how to assess prediction reliability based solely on available data.

Contribution

It introduces a Bayesian approach using a transformation group prior for particle classification and evaluates its performance under various prior knowledge scenarios.

Findings

Bayesian method performs well with different prior knowledge levels

Comparison shows advantages over nearest neighbor and kernel density methods

Discussion on data-driven reliability assessment

Abstract

The Bayesian approach to the prediction of particle type given measurements of particle location is explored, using a parametric model whose prior is based on the transformation group. Two types of particle are considered, and locations are expressed in terms of a single spatial coordinate. Several cases corresponding to different states of prior knowledge are evaluated, including the effect of measurement uncertainty. Comparisons are made to nearest neighbor classification and kernel density estimation. How one can evaluate the reliability of the prediction solely from the available data is discussed.