 Legal probabilism is a theoretical framework that helps to address these different questions. Finkelstein and Fairley (1970) gave one of the first systematic analyses of how probability theory, and Bayes’ theorem in particular, can help to evaluate evidence at trial (see  Section 1).  After the discovery of DNA fingerprinting, many legal probabilists focused on how probability theory could be used to quantify the strength of a DNA match (see  Section 2).  Recent work in Artificial Intelligence made it possible to use probability theory—in the form of Bayesian networks—to evaluate complex bodies of evidence consisting of multiple components (see  Section 3).  Following the work of Lempert (1977), likelihood ratios are now commonly used as probabilistic measures of the relevance of the evidence presented at trial (see  Section 4).  Other authors, starting with seminal papers by Kaplan (1968) and Cullison (1969), deployed probability theory and decision theory to model decision-making and standards of proof at trial (see  Section 5).