Bayesian Prediction for The Winds of Winter

TL;DR

This paper employs Bayesian random effects models to predict the distribution of point-of-view chapters for characters in the upcoming novels of George R. R. Martin's series, based on analysis of previous books.

Contribution

It introduces a Bayesian approach to forecast character-focused chapters in future novels, leveraging historical data from earlier books in the series.

Findings

Effective prediction of chapter counts for characters

Bayesian models capture variability across characters

Method can inform readers and writers about narrative focus

Abstract

Predictions are made for the number of chapters told from the point of view of each character in the next two novels in George R. R. Martin's \emph{A Song of Ice and Fire} series by fitting a random effects model to a matrix of point-of-view chapters in the earlier novels using Bayesian methods. {\textbf{SPOILER WARNING: readers who have not read all five existing novels in the series should not read further, as major plot points will be spoiled.}}