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Paul Chambers applies text mining analytics and Bayesian analysis to the Shakespeare authorship question. Based on machine learning and artificial intelligent algorithms, he illustrates the use of textual analytics to compare the poetry of Oxford to the poetic works attributed to William Shakespeare. Historical and literary sources are used only to provide a starting point for the Bayesian analysis. Using only timing events and statistical text based analytical tools, a relative Bayesian inference calculation is performed comparing the relative likelihoods of authorship between Oxford and the man from Stratford. This blend of science, mathematics, and culture demonstrate that the Earl of Oxford is orders-of-magnitude more likely to be the author.
Bio: Paul Chambers is an expert in data science and statistics with a Master’s degree in Physics from the University of Maryland at College Park and a PhD in Nuclear Engineering. He has served as a Senior Data Scientist with numerous firms including EEOC, CMS, Hitachi Consulting and is currently with Blockchains Inc.
Learn more at shakespeareoxfordfellowship.org
Employing Mathematics to Identify the Real Shakespeare by Paul Chambers: shakespeareoxfordfellowship.o...