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This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
This book constitutes the refereed proceedings of the 14th IAPR TC-18 International Conference on Discrete Geometry for Computer Imagery, DGCI 2008, held in Lyon, France, in April 2008. The 23 revised full papers and 22 revised poster papers presented together with 3 invited papers were carefully reviewed and selected from 76 submissions. The papers are organized in topical sections on models for discrete geometry, discrete and combinatorial topology, geometric transforms, discrete shape representation, recognition and analysis, discrete tomography, morphological analysis, discrete modelling and visualization, as well as discrete and combinatorial tools for image segmentation and analysis.
Handbook of Combinatorics
This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
Graph Theory (as a recognized discipline) is a relative newcomer to Mathematics. The first formal paper is found in the work of Leonhard Euler in 1736. In recent years the subject has grown so rapidly that in today's literature, graph theory papers abound with new mathematical developments and significant applications.As with any academic field, it is good to step back occasionally and ask Where is all this activity taking us?, What are the outstanding fundamental problems?, What are the next important steps to take?. In short, Quo Vadis, Graph Theory?. The contributors to this volume have together provided a comprehensive reference source for future directions and open questions in the field.
It is only during the last decade that the functions of sinusoidal endothelial cells, Kupffer cells, hepatic stellate cells, pit cells and other intrahepatic lymphocytes have been better understood. The development of methods for isolation and co-culturing various types of liver cells has established that they communicate and cooperate via secretion of various intercellular mediators. This monograph summarizes multiple data that suggest the important role of cellular cross-talk for the functions of both normal and diseased liver. Special features of the book include concise presentation of the majority of detailed data in 19 tables. Original schemes allow for the clear illustration of complicated intercellular relationships. This is the first ever presentation of the newly emerging field of liver biology, which is important for hepatic function in health and disease and opens new avenues for therapeutic interventions.
The comprehensive introduction to the art and science of locating facilities to make your organization more efficient, effective, and profitable. For the professional siting facilities, the task of translating organizational goals and objectives into concrete facilities requires a working familiarity with the theoretical and practical fundamentals of facility location planning and modeling. The first hands-on guide to using and developing facility location models, Network and Discrete Location offers a practiceoriented introduction to model-building methods and solution algorithms, complete with software to solve classical problems of realistic size and end-of-chapter exercises to enhance the reader's understanding. The text introduces the reader to the key classical location problems (covering, center, median, and fixed charge) which form the nucleus of facility location modeling. It also discusses real-life extensions of the basic models used in locating: production and distribution facilities, interacting services and facilities, and undesirable facilities. The book outlines a host of methodological tools for solving location models and provides insights into when each approach is useful and what information it provides. Designed to give readers a working familiarity with the basic facility location model types as well as an intuitive knowledge of the uses and limits of modeling techniques, Network and Discrete Location brings students and professionals alike swiftly from basic theory to technical fluency.
Full coverage of the Statistics unit is provided in a separate book which covers everything your students need for this option.
This book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.