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Probabilistic Similarity Networks

that encode knowledge in a decision-theoretic framework. He introduces the similarity network and partition and constructs the expert system;

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Probabilistic Methods for Bioinformatics

distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for;

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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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Similarity Search and Applications

This book constitutes the proceedings of the 9th International Conference on Similarity Search and Applications, SISAP 2016, held in Tokyo;

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Randomly Deployed Wireless Sensor Networks

Deployed Wireless Sensor Networks offers a probabilistic method to model and analyze these networks. The book considers the network design;

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Heidi

This textbook covers Shannon's theory of information as well as neural networks and probabilistic data modelling.;

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Apprentices of Wonder

Discusses the concept of neural networks, examines their similarity to the human mind, and looks at current research;

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

; logics; orthopairs; possibilistic networks; and probabilistic logics, probabilistic reasoning.;

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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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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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Statistical Analysis of Graph Structures in Random Variable Networks

measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also;

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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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Data-Driven Computational Neuroscience

book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification;

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Preference, Belief, and Similarity

sections: Similarity, Judgment, and Preferences. The Preferences section is subdivided into Probabilistic Models of Choice, Choice under Risk and;

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Probabilistic Data Structures for Blockchain-Based Internet of Things Applications

This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the;

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Probabilistic Networks and Expert Systems

Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while;

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Bayesian Inverse Problems

Gain a fundamental understanding of the use of probabilistic methods and reliability analysis techniques in engineering applications;

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Information Processing and Management of Uncertainty

measures and integrals; non-classical logics; data analysis; real-world applications; aggregation; probabilistic networks; recommendation systems;

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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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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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Probabilistic Graphical Models

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical;

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Probabilistic Graphical Models

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical;

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Visual Analysis of Behaviour

processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector;

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Practical Probabilistic Programming

the future! Probabilistic programming is a programming paradigm in which code models are used to draw probabilistic inferences from data. By;

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Large Scale Networks

: the self-similarity in traffic behaviour and the scale-free characteristic of a complex network. Additionally, the authors propose a new;

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Large Scale Networks

: the self-similarity in traffic behaviour and the scale-free characteristic of a complex network. Additionally, the authors propose a new;

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