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Dialogue Games for Inconsistent and Biased Information
Dialogue Games for Inconsistent and Biased Information

MATHEMATICS OF COMPUTATION Volume 72, Number 241, Pages 131–157 S 0025-5718(01)01371-0
MATHEMATICS OF COMPUTATION Volume 72, Number 241, Pages 131–157 S 0025-5718(01)01371-0

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File - Sheffield Peer Teaching Society

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Semi-Supervised Learning Using Gaussian Fields and Harmonic

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Semi-Supervised Learning Using Gaussian Fields and Harmonic

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Efficient Neural Codes under Metabolic Constraints

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Contrast Functions for Blind Separation and Deconvolution of Sources

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Scientific Visualization versus Information Visualization

... the original dataset. This requirement is not listed above, which makes VTK unsuitable for Information Visualization. However, many Scientific Visualization analysts study “Computational Steering,” with which feedback can be given to running simulations, based on visualizations of early results from ...
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"Lecture 1: Introduction to Random Walks and Diffusion."

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Understanding the concept of outlier and its relevance to the

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Partially Observable Markov Decision Processes with Reward

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1 Gambler`s Ruin Problem

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View PDF - CiteSeerX

Self-Improving Algorithms Nir Ailon Bernard Chazelle Seshadhri Comandur
Self-Improving Algorithms Nir Ailon Bernard Chazelle Seshadhri Comandur

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Information theory

Information theory is a branch of applied mathematics, electrical engineering, and computer science involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Since its inception it has broadened to find applications in many other areas, including statistical inference, natural language processing, cryptography, neurobiology, the evolution and function of molecular codes, model selection in ecology, thermal physics, quantum computing, linguistics, plagiarism detection, pattern recognition, anomaly detection and other forms of data analysis.A key measure of information is entropy, which is usually expressed by the average number of bits needed to store or communicate one symbol in a message. Entropy quantifies the uncertainty involved in predicting the value of a random variable. For example, specifying the outcome of a fair coin flip (two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a die (six equally likely outcomes).Applications of fundamental topics of information theory include lossless data compression (e.g. ZIP files), lossy data compression (e.g. MP3s and JPEGs), and channel coding (e.g. for Digital Subscriber Line (DSL)). The field is at the intersection of mathematics, statistics, computer science, physics, neurobiology, and electrical engineering. Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones, the development of the Internet, the study of linguistics and of human perception, the understanding of black holes, and numerous other fields. Important sub-fields of information theory are source coding, channel coding, algorithmic complexity theory, algorithmic information theory, information-theoretic security, and measures of information.
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