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Mutual information
Results: 1409

#	Item
401	Guidance Note GN010 Mutual Recognition of Interstate Induction Cards This information explains mutual recognition of interstate induction for construction work cards. Mutual recognition arrangements enhance the capacity Add to Reading List Source URL: worksafe.tas.gov.au Language: English - Date: 2015-01-20 17:15:58 Playing card Payment systems
402	MUTUAL NON-DISCLOSURE AGREEMENT This Mutual Non-Disclosure Agreement (the "Agreement"), effective as of ___________ ________ (the "Effective Date"), is entered into by and between Theory Four, LLC d/b/a Mode Set™, a Co Add to Reading List Source URL: modeset.com Language: English - Date: 2015-03-02 11:27:59 Secrecy Information sensitivity Labour law Non-disclosure agreement Confidentiality Trade secret Know-how Severability Legal professional privilege in England and Wales Intellectual property law Law Ethics
403	EN 257: Applied Stochastic Processes Problem Set 1 Douglas Lanman [removed] 2 February 2007 Add to Reading List Source URL: mesh.brown.edu Language: English - Date: 2007-02-09 13:44:18 Ordinary differential equations Expected value Probability density function Dirac delta function Random variable Quantities of information Mutual information Mathematical analysis Mathematics Probability theory
404	In Proceedings of the 25th International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK Acting Irrationally to Improve Performance in Stochastic Worlds Roman V. Belavkin Add to Reading List Source URL: www.eis.mdx.ac.uk Language: English - Date: 2006-03-08 07:20:19 Game theory Markov processes Economics Mutual information Utility Preference Expected utility hypothesis Markov chain Rational choice theory Statistics Decision theory Information theory
405	CS 195-5: Machine Learning Problem Set 5 Douglas Lanman [removed] 26 November 2006 Add to Reading List Source URL: mesh.brown.edu Language: English - Date: 2006-11-28 13:11:01 Gaussian function Kernel density estimation Mutual information Normal distribution Entropy Correlation and dependence Conditional entropy Information theory Statistics Probability and statistics
406	6 Random Systems So far we have been studying deterministic systems. But the world around us is not very deterministic; there are fluctuations in everything from a cup of coffee to the global eclonomy. In principle, the Add to Reading List Source URL: fab.cba.mit.edu Language: English - Date: 2014-02-25 03:45:13 Probability theory Randomness Statistical theory Generating functions Mutual information Normal distribution Independence Moment-generating function Joint probability distribution Statistics Information theory Probability and statistics
407	Part III Physics exams 2004–2006 Information Theory, Pattern Recognition and Neural Networks Part III Physics exams[removed] Add to Reading List Source URL: wol.ra.phy.cam.ac.uk Language: English - Date: 2007-03-08 17:56:25 Mathematics Mutual information Entropy Exponential distribution Conditional entropy Maximum likelihood Noisy-channel coding theorem Kullback–Leibler divergence Z-channel Information theory Statistics Information
408	Effective Supply Chain Management Case Study: Mutual Distributing Company Add to Reading List Source URL: www.dimins.com Language: English - Date: 2015-04-01 13:06:49 Information technology Information Online analytical processing Dimensional Insight Dashboard Data management Business intelligence Business
409	In Geometric Science of Information, 2013, Paris. Law of Cosines and Shannon-Pythagorean Theorem for Quantum Information? Roman V. Belavkin1 School of Engineering and Information Sciences Add to Reading List Source URL: www.eis.mdx.ac.uk Language: English - Date: 2013-05-23 12:46:39 Mathematics Statistical theory Kullback–Leibler divergence Logarithm Information geometry Divergence Pythagorean theorem Entropy Conditional mutual information Statistics Geometry Information theory
410	Solutions: 1: The mutual information between X and Y is I(X; Y ) ≡ H(X) − H(X\|Y ), and satisfies I(X; Y ) = I(Y ; X), and I(X; Y ) ≥ 0. It measures the average [1] Add to Reading List Source URL: wol.ra.phy.cam.ac.uk Language: English - Date: 2008-04-06 04:21:32 Statistical theory Probability and statistics Logarithms Estimation theory Randomness Kullback–Leibler divergence Likelihood function Entropy Mutual information Statistics Information theory Mathematics

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