By Brendan J. Frey

A number of difficulties in laptop studying and electronic conversation deal
with complicated yet dependent typical or man made structures. during this booklet, Brendan
Frey makes use of graphical types as an overarching framework to explain and solve
difficulties of development type, unsupervised studying, info compression, and
channel coding. utilizing probabilistic constructions similar to Bayesian trust networks and
Markov random fields, he's in a position to describe the relationships among random
variables in those structures and to use graph-based inference innovations to develop
new algorithms. one of the algorithms defined are the wake-sleep set of rules for
unsupervised studying, the iterative turbodecoding set of rules (currently the best
error-correcting interpreting algorithm), the bits-back coding procedure, the Markov chain
Monte Carlo method, and variational inference.

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Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series) by Brendan J. Frey


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