Abstract

Likelihoods — the probabilities treated as a function of their parameters — are a key concept in statistical decision theory. When applied to signal detection, they give a general method for evaluating evidence and determining optimal performance. This chapter describes the likelihood approach to signal-detection theory and how to analyze non-Gaussian distributions of evidence. The theory of the ideal observer is extended to Bayesian methods.