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Many models of communication include the idea that a sender encodes a message and uses a channel to transmit it to a receiver. Noise may distort the message along the way. The receiver then decodes the message and gives some form of feedback. [1] Models of communication simplify or represent the process of communication.
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as ...
Communication theories vary substantially in their epistemology, and articulating this philosophical commitment is part of the theorizing process. [1] Although the various epistemic positions used in communication theories can vary, one categorization scheme distinguishes among interpretive empirical, metric empirical or post-positivist, rhetorical, and critical epistemologies. [13]
The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization.
In numerous publications on Bayesian experimental design, it is (often implicitly) assumed that all posterior probabilities will be approximately normal. This allows for the expected utility to be calculated using linear theory, averaging over the space of model parameters. [2]
D. Lawrence Kincaid (born 1945) is an American communication researcher who originated the convergence theory of communication. He was a senior advisor for the Research and Evaluation Division of the Center for Communication Programs and an associate scientist in the Faculty of Social and Behavioral Sciences at the Johns Hopkins Bloomberg School of Public Health.
The likelihood estimate needs to be as large as possible; because it's a lower bound, getting closer improves the approximation of the log likelihood. By substituting in the factorized version of , (), parameterized over the hidden nodes as above, is simply the negative relative entropy between and plus other terms independent of if is defined as
It was later published in 1949 as a book titled The Mathematical Theory of Communication (ISBN 0-252-72546-8), which was published as a paperback in 1963 (ISBN 0-252-72548-4). The book contains an additional article by Warren Weaver, providing an overview of the theory for a more general audience. [12]