Bifalgorithm post · Brisk · Chapter 1
How can Bayes’ Theorem identify the likely author of a document?
How can Bayes’ Theorem identify the likely author of a document? (Brisk · Chapter 1) — "Anyway, all the stuff about diseases and tests is a side note. The main…
Ideas in this flowchart
- Bayes’ theorem exchanges conditional probabilities to make difficult inferences tractable.
- The theorem recasts a search for Pr( X | Y ) as a search for Pr( Y | X ), which is often easier to quantify.
- A computer science application of Bayes’ theorem is text mining.
- Text mining analyzes words in documents to classify them or summarize and infer their contents.
- A specific goal is identifying the true author of a document from samples of suspected authors’ writing.
- The Federalist Papers were jointly authored by Hamilton, Madison, and Jay, but the authorship of specific essays was uncertain.
- The task is to determine which of the three authors wrote essay #84.
- The direct approach compares Pr(Hamilton | essay84), Pr(Madison | essay84), and Pr(Jay | essay84), choosing the highest probability.
- Directly finding probabilities such as Pr(Hamilton | essay84) is impossible to know from the essay alone.
- Bayes’ theorem reformulates the question as Pr(essay84 | Hamilton), and analogously for the other candidates.
- Known samples of each author’s writing make these likelihoods accessible.
- Relevant features include characteristic phrases, vocabulary usage, and the number of sentences per paragraph.
- Comparing these features with the candidates’ styles allows estimation of relative authorship probabilities; this exchange is the technique’s backbone.
