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Bayesian Updating and the “Picture Becomes Clearer” Analogy
Probability

Bayesian Updating and the “Picture Becomes Clearer” Analogy

Author(s): David Aldous, Ph.D.

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Notes


Bio: David Aldous is Professor in the Statistics Dept at U.C. Berkeley, since 1979. He received his Ph.D. from Cambridge University in 1977. He is the author of “Probability Approximations via the Poisson Clumping Heuristic” and (with Jim Fill) of a notorious unfinished online work “Reversible Markov Chains and Random Walks on Graphs”. His research in mathematical probability has covered weak convergence, exchangeability, Markov chain mixing times, continuum random trees, stochastic coalescence, and spatial random networks. A central theme has been the study of large finite random structures, obtaining asymptotic behavior as the size tends to infinity via consideration of some suitable infinite random structure. He has recently become interested in articulating critically what mathematical probability says about the real world.

He is a Fellow of the Royal Society and a foreign associate of the National Academy of Sciences.


Bayesian Updating and the “Picture Becomes Clearer” Analogy was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI

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