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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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Probabilistic Graphical Models for Computer Vision.

Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and;

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Probabilistic Graphical Models

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available;

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Probabilistic Graphical Models

undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing;

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Probabilistic Graphical Models

undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing;

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Practical Probabilistic Programming

trends, computer system failures, experimental outcomes, and other critical concerns. Practical Probabilistic Programming explains;

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Computational Learning And Probabilistic Reasoning

techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational;

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Data-Driven Computational Neuroscience

. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning;

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Handbook of Probabilistic Models

that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil;

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Network Reliability and Algebraic Structures

Networks of computer systems, distribution systems, and telecommunication systems play an increasingly important role in our lives. This;

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Biological Sequence Analysis

probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from;

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Artificial Intelligence in Medicine

in June 1995. The volume contains 32 full refereed selected papers contributed by researchers and professionals coming from computer science;

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Bayesian Networks and Decision Graphs

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical;

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Probabilistic Reliability Analysis of Power Systems: A Student's Introduction

This textbook provides an introduction to probabilistic reliability analysis of power systems. It discusses a range of probabilistic;

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Probabilistic Reliability Analysis of Power Systems

This textbook provides an introduction to probabilistic reliability analysis of power systems. It discusses a range of probabilistic;

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Visual Analysis of Behaviour

connecting-the-dots for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, bag-of-words representation;

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Probabilistic Graphical Models

This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective;

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Learning Probabilistic Graphical Models in R

* Predict and use a probabilistic graphical models (PGM) as an expert system * Comprehend how your computer can learn Bayesian modeling to solve;

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Random Graphs and Complex Networks

This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive;

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Probabilistic Reasoning in Multiagent Systems

probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an;

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Probabilistic Reasoning in Multiagent Systems

probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an;

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Probability in Electrical Engineering and Computer Science

computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and;

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Neural Network Learning

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised;

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Neural Network Learning

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised;

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Performance Analysis of Queuing and Computer Networks

Performance Analysis of Queuing and Computer Networks develops simple models and analytical methods from first principles to evaluate;

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Performance Analysis of Queuing and Computer Networks

Performance Analysis of Queuing and Computer Networks develops simple models and analytical methods from first principles to evaluate;

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Structured Learning and Prediction in Computer Vision

models which are covered in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference;

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