Jump to ContentJump to Main Navigation

You are looking at 1-5 of 5 items

  • Keywords: Kullback-Leibler divergence x
Clear All Modify Search

View:

Introduction to Information Theory

M. Vidyasagar

in Hidden Markov Processes: Theory and Applications to Biology

Published in print:
2014
Published Online:
October 2017
ISBN:
9780691133157
eISBN:
9781400850518
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691133157.003.0002
Subject:
Mathematics, Probability / Statistics

This chapter provides an introduction to some elementary aspects of information theory, including entropy in its various forms. Entropy refers to the level of uncertainty associated with a random ... More


Integrated Objective Bayesian Estimation and Hypothesis Testing

José M. Bernardo

in Bayesian Statistics 9

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199694587
eISBN:
9780191731921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199694587.003.0001
Subject:
Mathematics, Probability / Statistics

The complete final product of Bayesian inference is the posterior distribution of the quantity of interest. Important inference summaries include point estimation, region estimation and precise ... More


Updating, Supposing, and MAXENT

Brian Skyrms

in From Zeno to Arbitrage: Essays on Quantity, Coherence, and Induction

Published in print:
2012
Published Online:
January 2013
ISBN:
9780199652808
eISBN:
9780191745829
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199652808.003.0010
Subject:
Philosophy, Philosophy of Science, Metaphysics/Epistemology

Is minimizing the Kullback-Leibler divergence subject to a constraint a rule of inductive inference, as some have proposed. This chapter suggests that it is better seen as a method for counterfactual ... More


Semi-Supervised Learning with Conditional Harmonic Mixing

Burges Christopher J. C. and Platt John C.

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0014
Subject:
Computer Science, Machine Learning

This chapter introduces a general probabilistic formulation called conditional harmonic mixing (CHM), in which the links are directed, a conditional probability matrix is associated with each link, ... More


A Probability Primer

Kenji Doya and Shin Ishii

in Bayesian Brain: Probabilistic Approaches to Neural Coding

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262042383
eISBN:
9780262294188
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262042383.003.0001
Subject:
Neuroscience, Disorders of the Nervous System

This chapter introduces the Bayesian theorem of probability, highlights its importance in our understanding of how the brain processes information, and also discusses probability distribution and ... More


View: