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Analyzing and Modeling Rank Data

, sociology, economics, and biology. This book systematically presents the basic models and methods for analyzing data in the form of ranks;

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Low Rank and Sparse Modeling for Visual Analysis

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding;

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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem;

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Analyzing Data Through Probabilistic Modeling in Statistics

connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and;

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Modeling and Simulation for Analyzing Global Events

qualitative analysis. With an emphasis on gathering and mapping empirical data, the authors detail the steps needed for accurately analyzing global;

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Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences

This book reviews methods of conceptualizing, measuring, and analyzing interdependent data in developmental and behavioral sciences;

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Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences

This book reviews methods of conceptualizing, measuring, and analyzing interdependent data in developmental and behavioral sciences;

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Analyzing Longitudinal Clinical Trial Data: A Practical Guide

Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest;

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Analyzing Longitudinal Clinical Trial Data: A Practical Guide

Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest;

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Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases

of Infectious Diseases features: * Approaches to better use infectious disease data collected from various sources for analysis and modeling purposes;

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Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology

of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation;

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Handbook for Applied Modeling

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;

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Analyzing Ecological Data

-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series;

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Analyzing Ecological Data

-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series;

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Handbook for Applied Modeling

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;

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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Graph-Powered Machine Learning

neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon;

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Statistics, Data Analysis And Decision Modeling

This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical;

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Robust Representation for Data Analytics

the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including;

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Recent Advances In Stochastic Modeling And Data Analysis

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover;

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Bayesian Ideas And Data Analysis

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and;

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Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their;

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Hierarchical Modeling and Analysis for Spatial Data

Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first;

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Fuzzy Logic-Based Modeling in Collaborative and Blended Learning

in order to analyze the learning process, gauge student feedback, and enable quality learning outcomes. Focusing on educational data analysis and;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Rank-Based Methods for Shrinkage and Selection

selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and;

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Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;

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