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An Algebraic and Probabilistic Framework for Network Information Theory

theory and communications. It provides an in-depth discussion of the theory and techniques resulting in a framework that the reader can apply to;

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Probabilistic Similarity Networks

In this blend of formal theory and practical application, Heckerman develops methods for building normative expert systems - expert systems;

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Information Theory & Coding By Example

This fundamental monograph introduces both the probabilistic and algebraic aspects of information theory and coding. It has evolved from;

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Probabilistic models for computer networks

Probabilistic models for computer networks: Tools and solved problems overviews the main probabilistic tools and theory used in the;

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Information Theory And Coding By Example

This fundamental monograph introduces both the probabilistic and algebraic aspects of information theory and coding. It has evolved from;

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Network Reliability and Algebraic Structures

identifying a number of algebraic structures that serve to unify the study of networks, and to suggest new techniques and procedures for analysing the;

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Advanced Quantum Communications

theory and quantum communication networks. The authors present the fundamental theoretical results of quantum information theory, and;

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Network Coding and Subspace Designs

network coding, in which the network is not simply viewed as a mechanism for delivering packets, but rather an algebraic structure named the;

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Algebraic Geometry Codes

of mathematics. On one hand, it involves such classical areas as algebraic geometry and number theory; on the other, it is connected to information;

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LDPC Code Designs, Constructions, and Unification

both algebraic- and graph-based approaches into a single theoretical framework (the superposition construction). An algebraic method for;

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Inferential Models

developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the;

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Inferential Models

approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to;

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Advances in Algebra

, spherical and algebraic spaces, and tropical algebraic geometry. Since 1988, SRAC has been an important event for the algebra research community;

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Probabilistic Inference and Statistical Methods in Network Analysis

dimensions of probabilistic inferences. It includes an in-depth social network analysis over clinical education of diabetic foot and also on online;

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Distributions in Stochastic Network Models

leads to an appearance of new original stochastic network models, algorithms and application to computer science and information technologies;

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Fuzzy Decision Making Methods Based on Prospect Theory and Its Application in Ve

probabilistic hesitant fuzzy preference information, Improved TODIM based on prospect theory and the improved TODIM with probabilistic hesitant fuzzy;

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Fuzzy Decision Making Methods Based on Prospect Theory and Its Application in Ve

probabilistic hesitant fuzzy preference information, Improved TODIM based on prospect theory and the improved TODIM with probabilistic hesitant fuzzy;

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Lattice Coding For Signals And Networks

Unifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for;

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Network Coding

Network coding is a field of information and coding theory and is a method of attaining maximum information flow in a network. This book;

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Network-Aware Source Coding And Communication

description coding, this is an ideal self-contained resource for researchers and students in information theory and network theory.;

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Bayesian Networks Handbook

A Bayesian network is also known as a Bayes network, belief network or causal probabilistic network. Bayesian belief networks are effective;

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Completeness and Reduction in Algebraic Complexity Theory

This is a thorough and comprehensive treatment of the theory of NP-completeness in the framework of algebraic complexity theory. Coverage;

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Graph Theory

structuring the contents of mathematical subjects using graph theory; and a modularity-based filtering approach for network immunisation.;

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Combinatorial Methods With Computer Applications

focuses on graph theory, covering topics such as trees, isomorphism, automorphism, planarity, coloring, and network flows. The final chapters;

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The Chip Is the Network

, and suggesting a few open-ended problems. The Chip Is the Network is an invaluable reference for the NoC research community and, indeed;

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Tensors

basics of quantum information theory, Strassen's laser method for matrix multiplication, and moment maps in algebraic geometry. It also details;

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Computation with Linear Algebraic Groups

combines in one single text both an introduction to the basic theory of linear algebraic groups and a substantial collection of useful algorithms;

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