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Learn likelihood and Bayesian stats, visually

July 30, 2012

My friend Simon Prince has just finished a new book on computer vision. This kind of thing may seem a little off-topic for this blog, but while working on my PhD, I found earlier drafts of Simon’s book to be extremely useful for my work on random effects ordination — especially the bits on latent variable models. Simon writes from a likelihood / Bayesian perspective. For machine vision students, this approach provides a compelling mathematical framework with which to assimilate the broad range of ideas and concepts in their field. However, a perhaps less explicit benefit is that the visual nature of the subject matter may illuminate the general quantitative concepts in likelihood and Bayesian stats — this was the case for me! In other words, the examples in the book helped me to associate mental pictures with general quantitative ideas. So I think the book may have broad appeal beyond its intended audience of students in machine vision.

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