Computational and statistical methods for chemical engineering, Krijnen, Wim P. , Wit, Ernst C.
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Описание: This book presents an introduction to Evolutionary Game Theory (EGT) which is an emerging field in the area of complex systems attracting the attention of researchers from disparate scientific communities.
Автор: Antonio Lagana; Gregory A. Parker Название: Chemical Reactions ISBN: 3319872990 ISBN-13(EAN): 9783319872995 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Поставка под заказ.
Описание: This graduate textbook, written by experienced lecturers, features the study and computation of efficient reactive processes. The text begins with the problem of determining the chemical reaction properties by first decomposing complex processes into their elementary components. Next, the problem of two colliding mass points is investigated and relationships between initial conditions and collision outcomes are discussed. The failure of classical approaches to match experimental information is discussed and a quantum formulation of the calculation of the properties of two colliding bodies is provided. The authors go onto describe how the formalism is extended to structured collision partners by discussing the methods used to compute the electronic structure of polyelectronic reactants and products and the formalism of atom diatom reactions. Additionally, the relationships between the features of the potential energy surface and the outcomes of the reactive dynamics, are discussed.
Methods for computing quantum, classical, and semi-classical reactive probabilities based on the already discussed concepts and tools are also featured and the resulting main typical reactive behaviors are analyzed. Finally, the possibility of composing the computational tools and technologies needed to tackle more complex simulations as well as the various competences and distributed computing infrastructure needed for developing synergistic approaches to innovation are presented.
Описание: This volume contains original and refereed contributions from the tenth AMCTM Conference (http://www.nviim.ru/AMCTM2014) held in St. Petersburg (Russia) in September 2014 on the theme of advanced mathematical and computational tools in metrology and testing.
Описание: What every neuroscientist should know about the mathematical modeling of excitable cells. Combining empirical physiology and nonlinear dynamics, this text provides an introduction to the simulation and modeling of dynamic phenomena in cell biology and neuroscience. It introduces mathematical modeling techniques alongside cellular electrophysiology. Topics include membrane transport and diffusion, the biophysics of excitable membranes, the gating of voltage and ligand-gated ion channels, intracellular calcium signalling, and electrical bursting in neurons and other excitable cell types. It introduces mathematical modeling techniques such as ordinary differential equations, phase plane, and bifurcation analysis of single-compartment neuron models. With analytical and computational problem sets, this book is suitable for life sciences majors, in biology to neuroscience, with one year of calculus, as well as graduate students looking for a primer on membrane excitability and calcium signalling.
A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field
This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference, providing complete derivations of results, discussions, and MATLAB programs for computation. It emphasizes details of the relevance of the material, intuition, and discussions with a view towards very modern statistical inference. In addition to classic subjects associated with mathematical statistics, topics include an intuitive presentation of the (single and double) bootstrap for confidence interval calculations, shrinkage estimation, tail (maximal moment) estimation, and a variety of methods of point estimation besides maximum likelihood, including use of characteristic functions, and indirect inference. Practical examples of all methods are given. Estimation issues associated with the discrete mixtures of normal distribution, and their solutions, are developed in detail. Much emphasis throughout is on non-Gaussian distributions, including details on working with the stable Paretian distribution and fast calculation of the noncentral Student's t. An entire chapter is dedicated to optimization, including development of Hessian-based methods, as well as heuristic/genetic algorithms that do not require continuity, with MATLAB codes provided.
The book includes both theory and nontechnical discussions, along with a substantial reference to the literature, with an emphasis on alternative, more modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given to alternative model selection methods, though hypothesis testing of distributional assumptions is covered in detail, notably for the normal distribution.
Presented in three parts--Essential Concepts in Statistics; Further Fundamental Concepts in Statistics; and Additional Topics--Fundamental Statistical Inference: A Computational Approach offers comprehensive chapters on: Introducing Point and Interval Estimation; Goodness of Fit and Hypothesis Testing; Likelihood; Numerical Optimization; Methods of Point Estimation; Q-Q Plots and Distribution Testing; Unbiased Point Estimation and Bias Reduction; Analytic Interval Estimation; Inference in a Heavy-Tailed Context; The Method of Indirect Inference; and, as an appendix, A Review of Fundamental Concepts in Probability Theory, the latter to keep the book self-contained, and giving material on some advanced subjects such as saddlepoint approximations, expected shortfall in finance, calculation with the stable Paretian distribution, and convergence theorems and proofs.
Автор: Thisted Название: Elements of Statistical Computing ISBN: 0412013711 ISBN-13(EAN): 9780412013713 Издательство: Taylor&Francis Рейтинг: Цена: 27562.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistics and computing share many close relationships
Автор: Little Название: Statistical Analysis with Missing Data, Third Edit ion ISBN: 0470526793 ISBN-13(EAN): 9780470526798 Издательство: Wiley Рейтинг: Цена: 12664.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration.
"Computational and Statistical Methods for Analysing Big Data with Applications" starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.
Advanced computational and statistical methodologies for analysing big data are developed.
Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable.
Case studies are discussed to demonstrate the implementation of the developed methods.
Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation.
Computing code/programs are provided where appropriate.
Описание: * Presents a much-needed practical guide to statistical spatial analysis on a network, in a logical, user-friendly order. * Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.
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