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Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. This Special Issue of Mathematics (ISSN 2227-7390) includes diverse modern data analysis methods such as skew-reflected-Gompertz information quantifiers with application to sea surface temperature records, the performance of variable selection and classification via a rank-based classifier, two-stage classification with SIS using a new filter ranking method in high throughput data, an estimation of sensitive attribute applying geometric distribution under probability proportional to size sampling, combination of ensembles of regularized regression models with resampling-based lasso feature selection in high dimensional data, robust linear trend test for low-coverage next-generation sequence data controlling for covariates, and comparing groups of decision-making units in efficiency based on semiparametric regression.

Kullback–Leibler divergence --- geometric distribution --- accuracy --- AUROC --- allele read counts --- mixture model --- low-coverage --- entropy --- gene-expression data --- SCAD --- data envelopment analysis --- LASSO --- high-throughput --- sandwich variance estimator --- adaptive lasso --- semiparametric regression --- ?1 lasso --- Laplacian matrix --- elastic net --- feature selection --- sea surface temperature --- gene expression data --- Skew-Reflected-Gompertz distribution --- lasso --- next-generation sequencing --- BH-FDR --- stochastic frontier model --- ?2 ridge --- geometric mean --- resampling --- Gompertz distribution --- adapative lasso --- group efficiency comparison --- sensitive attribute --- MCP --- probability proportional to size (PPS) sampling --- randomization device --- SIS --- Yennum et al.’s model --- ensembles

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This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems. The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications. The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications. This book has been adopted as a textbook at the following universities: ? University of Stuttgart, Germany Royal Institute of Technology, Sweden Johannes Kepler University, Austria Georgia Tech, USA University of Washington, USA Ohio University, USA

Network analysis (Planning) --- Multiagent systems --- Agent-based model (Computer software) --- MASs (Multiagent systems) --- Multi-agent systems --- Systems, Multiagent --- Intelligent agents (Computer software) --- Project networks --- Planning --- System analysis --- Graphic methods. --- Mathematical models. --- Mathematical models --- Graphic methods --- Addition. --- Adjacency matrix. --- Algebraic graph theory. --- Algorithm. --- Automorphism. --- Bipartite graph. --- Cardinality. --- Cartesian product. --- Circulant graph. --- Combinatorics. --- Complete graph. --- Computation. --- Connectivity (graph theory). --- Controllability. --- Convex combination. --- Corollary. --- Cycle graph (algebra). --- Cycle space. --- Degree (graph theory). --- Degree matrix. --- Diagonal matrix. --- Diameter. --- Differentiable function. --- Dimension. --- Directed graph. --- Division by zero. --- Dynamical system. --- Eigenvalues and eigenvectors. --- Equilibrium point. --- Estimation. --- Estimator. --- Existential quantification. --- Extremal graph theory. --- Graph (discrete mathematics). --- Graph theory. --- Identity matrix. --- Incidence matrix. --- Information exchange. --- Initial condition. --- Interconnection. --- Iteration. --- Kalman filter. --- Kronecker product. --- LTI system theory. --- LaSalle's invariance principle. --- Laplacian matrix. --- Least squares. --- Line graph. --- Linear map. --- Lipschitz continuity. --- Lyapunov function. --- Lyapunov stability. --- Markov chain. --- Mathematical optimization. --- Matrix exponential. --- Measurement. --- Multi-agent system. --- Nash equilibrium. --- Natural number. --- Network topology. --- Nonnegative matrix. --- Notation. --- Observability. --- Optimal control. --- Optimization problem. --- Pairwise. --- Parameter. --- Path graph. --- Permutation matrix. --- Permutation. --- Positive semidefinite. --- Positive-definite matrix. --- Probability. --- Quantity. --- Random graph. --- Random variable. --- Rate of convergence. --- Requirement. --- Result. --- Robotics. --- Scientific notation. --- Sensor. --- Sign (mathematics). --- Simplicial complex. --- Special case. --- Spectral graph theory. --- Stochastic matrix. --- Strongly connected component. --- Subset. --- Summation. --- Supergraph. --- Symmetric matrix. --- Systems theory. --- Theorem. --- Theory. --- Unit interval. --- Upper and lower bounds. --- Variable (mathematics). --- Vector space. --- Without loss of generality.