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Simplicity and Complexity of Belief-Propagation

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If you have a question about this talk, please contact HoD Secretary, DPMMS .

There is a very simple algorithm for the inference of posteriors for probability models on trees. This algorithm, known as ``Belief Propagation” is widely used in coding theory, in machine learning, in evolutionary inference, among many other areas. The talk will be devoted to the analysis of Belief Propagation in some of the simplest probability models. We will highlight the interplay between Belief Propagation, linear estimators (statistics), the Kesten-Stigum bound (probability) and Replica Symmetry Breaking (statistical physics). We will show how the analysis of Belief Propagation allowed to proof phase transitions for phylogenetic reconstruction in evolutionary biology and develop optimal algorithms for inference of block models. Finally, we will discuss the computational complexity of this ``simple” algorithm.

A wine reception will follow this talk in the Central Core.

This talk is part of the Peter Whittle Lecture series.

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https://maths-cam-ac-uk.zoom.us/j/95451181978?pwd=QTY1WDRxaW92L1BpQm9uSXRyT3JyUT09 Lecture Theatre 1, Department of Computer Science and Technology, University of Cambridge, William Gates Building, 15 JJ Thomson Ave, Cambridge CB3 0FD Sanger Building Seminar Room

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