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Graphical Models for Security

This book constitutes revised papers from the 6th International Workshop on Graphical Models for Security, GraMSec 2019, held in Hoboken;

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Graphical Models with R

handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models;

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Probabilistic Graphical Models

graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and;

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Probabilistic Graphical Models

graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and;

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Handbook of Graphical Models

, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number;

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Handbook of Graphical Models

, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number;

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Graphical Models in Applied Multivariate Statistics

Graphical models----a subset of log--linear models----reveal the interrelationships between multiple variables and features of the;

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Graphical Models for Categorical Data

For advanced students of network data science, this compact account covers both well-established methodology and the theory of models;

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Probabilistic Graphical Models

graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end;

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Time-Like Graphical Models

probabilistic graphical models. In 2011, Burdzy and Pal proposed a continuous version of graphical models indexed by graphs with an embedded time;

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Learning Probabilistic Graphical Models in R

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book;

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Bayesian and grAphical Models for Biomedical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging;

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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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Mastering Probabilistic Graphical Models Using Python

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Advanced Methodologies for Bayesian Networks

researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic;

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Probabilistic Graphical Models

. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model;

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Causal Analysis in Biomedicine and Epidemiology

method of model parametritization. Illustrates structural equations and graphical elements for complex causal systems.;

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Causal Analysis in Biomedicine and Epidemiology

method of model parametritization. Illustrates structural equations and graphical elements for complex causal systems.;

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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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Graphical Models for Machine Learning and Digital Communication

book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised;

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

; inconsistency; graphical models; Bayesian networks; belief functions; logic; and probabilistic graphical models for scalable data analytics. Papers come;

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Statistical Relational Artificial Intelligence

has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus;

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Therapeutic Potential of Furin Inhibition

has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus;

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Statistical Relational Artificial Intelligence

has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus;

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Statistical Relational Artificial Intelligence

has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus;

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Econometric Society Monographs

graphical and numerical methods of specification testing. This is the first published exposition of current econometric methods for the study;

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Computational Methods for Deep Learning

other publications include the Springer title, Visual Cryptography for Image Processing and Security.;

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