A clear and efficient balance between theory and application of statistical modeling techniques in the social and behavioral sciences Written as a general and accessible introduction, Applied Univariate, Bivariate, and Multivariate Statistics provides an overview of statistical modeling techniques used in fields in the social and behavioral sciences.
This research monograph summarizes a line of research that maps certain classical problems of discrete mathematics and operations research - such as the Hamiltonian Cycle and the Travelling Salesman Problems - into convex domains where continuum analysis can be carried out.
Nonlinear Programming 3 covers the proceedings of the Special Interest Group on Mathematical Programming Symposium conducted by the Computer Sciences Department at the University of Wisconsin, Madison, on July 11-13, 1977.
Dieses Lehrbuch umfaßt einen Kanon von Themen, der an vielen Universitäten unter dem Titel "Diskrete Strukturen" fester Bestandteil des Informatik-Grundstudiums geworden ist.
Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data.
This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field.
Topics in Contemporary Mathematical Analysis and Applications encompasses several contemporary topics in the field of mathematical analysis, their applications, and relevancies in other areas of research and study.
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Large-Scale Scientific Computations, LSSC 2013, held in Sozopol, Bulgaria, in June 2013.
Statistics Explained, now in its fully revised Fourth Edition, is for students and researchers who wish to understand the statistical analyses used to analyse quantitative (numerical) research data in a wide range of academic disciplines, in particular, the behavioural, human and social sciences.
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction.
Presenting the first comprehensive review of the subject's theory and applications inmore than 15 years, this outstanding reference encompasses the most-up-to-date advancesin lognormal distributions in thorough, detailed contributions by specialists in statistics,business and economics , industry, biology , ecology, geology, and meteorology.
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning.
Stochastic differential equations in infinite dimensional spaces are motivated by the theory and analysis of stochastic processes and by applications such as stochastic control, population biology, and turbulence, where the analysis and control of such systems involves investigating their stability.
In recent years, there has been a tremendous development in the area of quantitative and statistical analysis of linguistic and literary data, generated, no doubt, by extensive advances in computer technology and their relatively easy availability to scholars.
Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor.
Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application of generalized linear mixed models and its related models in the statistical design and analysis of repeated measures from randomized controlled trials.
Focuses on commonly used and recently developed methods for designing and analyzing high-throughput screening experiments from a statistically sound basis.
This book presents broadly applicable methods for the large deviation and moderate deviation analysis of discrete and continuous time stochastic systems.
This book establishes a unified framework for dealing with typical engineering complications arising in modern, complex, large-scale networks such as parameter uncertainties, missing measurement and cyber-attack.
The second edition of Reverse Engineering of Algebraic Inequalities is a comprehensively updated new edition demonstrating the exploration of new physical realities in various unrelated domains of human activity through reverse engineering of algebraic inequalities.
This book aims to clarify the priorities of the Sendai Framework for the DRR 2015 - 2030, through gathering recent contributions addressing the different ways researchers define, measure, reduce, and manage risk in the challenge of the DRR.
Stochastic instantaneous volatility models such as Heston, SABR or SV-LMM have mostly been developed to control the shape and joint dynamics of the implied volatility surface.
Practical Numerical and Scientific Computing with MATLAB(R) and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming.
This book compares and contrasts the principles and practices of rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT).
This book provides advanced theoretical and applied tools for the implementation of modern micro-econometric techniques in evidence-based program evaluation for the social sciences.
