After examining regular probability, he examines the Bayes approach to probability. He shows the conditional probability equation first to provide a comparison. This equation is P(H/E)=P(HupsidedownUE)/P(E). The Bayes approach to probability is P(H/E)=P(E/H)P(H)/P(E). P(H/E) is revised probability after reviewing evidence. P(E/H) is the conditional probability of E in case of H. P(H) is the probability without any evidence. The Bayes approach allows for the estimate to change as more information comes in.
In intelligence analysis, the equation given is R=PL with R being the estimate of conditional probability of the hypothesis H after revising evidence E. P or prior estimate times the likelihood of an event or L. He advocates that this approach is best used in strategic warning; for example the probability of a terrorist attack. This approach forces analysts to quantify their estimates and reduce cognitive bias using competing hypotheses. The weaknesses of this approach in the vulnerability to false evidence, time constraints, and limited information.
He uses historical events such as the Cuban missile crisis and the tension between Russia and China during the Cold War to show the differences between conventional probability and the Bayes approach. The estimates were the same, but the analysts using the Bayes approach arrived at their conclusions faster than the conventional analysts.
Critique: Dr. Davide was very thorough in his study of the use of Bayesian networks in intelligence analysis. However, it was a difficult read because he uses mathematical language very often. His examples were also slightly outdated. An updated version of his study would be useful in the modern intelligence field.
Dr. Barbieri Davide. https://www.researchgate.net/publication/257933578_Bayesian_Intelligence_Analysis.