无刷直流电机矢量控制技术.txt

目录：第一章：电机技术成了战略技术；第二章：有刷直流电机的工作原理和特征、驱动电机；第三章：无刷直流电机的特征和工作原理；第四章：无刷直流电机驱动方式的进化；第五章：无刷直流电机矢量控制理论；。。。。。。此书唯一的缺陷是扫描版，不能编辑，大家酌情下载

MCMC and Applied Bayesian Statistics.pdf

Markov chain Monte Carlo is a stochastic simulation technique that is very useful for computing inferential quantities. It is often used in a Bayesian context, but not restricted to a Bayesian setting.

Chapter 4 Markov Chain Monte Carlo .pdf

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Markov Chains and Monte–Carlo Simulation.pdf

Markov chains – are a fundamental class of stochastic models for sequences of non–independent random variables, i.e. of random variables possessing a specific dependency structure. – have numerous applications e.g. in insurance and finance. – play also an important role in mathematical modelling and analysis in a variety of other fields such as physics, chemistry, life sciences, and material sciences.

Markov Chain Monte Carlo_ innovations and applications .pdf

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Markov Chains_ Analytic and Monte Carlo Computations .pdf

This book is written with a broad spectrum, that allows for different readings at various levels. It tries nevertheless to plunge quickly into the heart of the matter. The basic analytical tool is the maximum principle, which is natural in this setting. It is superfcially compared to martingale methods in some instances. The basic probabilistic tool is the Markov property, strong or not.

Stochastic Population Models.pdf

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Stochastic modelling for systems biology-CRC

This series aims to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical, statistical, and computational methods into biology by publishing a broad range of textbooks, reference works, and handbooks. The titles included in the series are meant to appeal to students, researchers, and professionals in the mathematical, statistical and computational sciences, fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the feld. The inclusion of concrete examples and applications, and programming techniques and examples, is highly encouraged.

A Markov Chain Monte Carlo Method for Inverse Stochastic Simulation

A classical two-stage method to stochastic inverse problems in groundwater and petroleum engineering starts from the generation of a series of independent seed flelds and then calibrates those flelds to inverse-condition on nonlinearly dependent state data from difierent sources, which is known as model calibration or history matching. However, an inherent deflciency exists in this type of method: the spatial structure and statistics are not preserved during the procedure of model calibration and history matching. While the spatial structure and statistics of models may be one of the most important error sources to the prediction of the future performance of reservoirs and aquifers, it should be consistent with the given information just as conditioning to linear data and inverse-conditioning to nonlinear data. In other words, the realizations generated should preserve the given spatial structure and statistics during the procedure of conditioning and inverse-conditioning. Aiming at this problem, a stochastic approach is presented in this study to generate independent, identically distributed (i.i.d) realizations which are not only conditional on static linear data and inverse-conditional on dynamic nonlinear data but also have the specifled spatial structure and statistics.

Handbook of approximate Bayesian computation.pdf

The objective of the series is to provide high-quality volumes covering the state-of-the-art in the theory and applications of statistical methodology. The books in the series are thoroughly edited and present comprehensive, coherent, and unified summaries of specific methodological topics from statistics. The chapters are written by the leading researchers in the field, and present a good balance of theory and application through a synthesis of the key methodological developments and examples and case studies using real data.