Statistics for Mining Engineering

Author: Jacek M. Czaplicki
Publisher: CRC Press
ISBN: 1138001139
Format: PDF, Docs
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Many areas of mining engineering gather and use statistical information, provided by observing the actual operation of equipment, their systems, the development of mining works, surface subsidence that accompanies underground mining, displacement of rocks surrounding surface pits and underground drives and longwalls, amongst others. In addition, the actual modern machines used in surface mining are equipped with diagnostic systems that automatically trace all important machine parameters and send this information to the main producer’s computer. Such data not only provide information on the technical properties of the machine but they also have a statistical character. Furthermore, all information gathered during stand and lab investigations where parts, assemblies and whole devices are tested in order to prove their usefulness, have a stochastic character. All of these materials need to be developed statistically and, more importantly, based on these results mining engineers must make decisions whether to undertake actions, connected with the further operation of the machines, the further development of the works, etc. For these reasons, knowledge of modern statistics is necessary for mining engineers; not only as to how statistical analysis of data should be conducted and statistical synthesis should be done, but also as to understanding the results obtained and how to use them to make appropriate decisions in relation to the mining operation. This book on statistical analysis and synthesis starts with a short repetition of probability theory and also includes a special section on statistical prediction. The text is illustrated with many examples taken from mining practice; moreover the tables required to conduct statistical inference are included.

Statistical Methods for Mineral Engineers

Author: Tim Napier-Munn
Publisher:
ISBN: 9780980362244
Format: PDF, Docs
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Written by a mineral engineer for mineral engineers, and packed with real world examples, this book de-mystifies the statistics that most of us learned at university and then forgot. It shows how simple statistical methods, most of them available in Excel, can be used to make good decisions in the face of experimental uncertainty. Written in accessible language, it explains how experimental uncertainty arises from the normal measurement errors and how statistics provides a powerful methodology to manage that uncertainty. It assumes only that the readers are numerate, can use Excel, and want to do a better professional job. It is aimed squarely at mineral engineers and allied professionals (such as chemists) on the mine site, in head office, in engineering and supply companies and in universities. Most of the examples are illustrated in Excel but Minitab is also used for advanced techniques. The book includes over 100 Excel and Minitab hints. Example spreadsheets can be downloaded from the JKMRC and JKTech websites.

Handbook of Statistical Analysis and Data Mining Applications

Author: Robert Nisbet
Publisher: Elsevier
ISBN: 0124166458
Format: PDF
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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Stochastic Modeling and Mathematical Statistics

Author: Francisco J. Samaniego
Publisher: CRC Press
ISBN: 1466560479
Format: PDF, Kindle
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Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well as students in the quantitative sciences. The book’s conversational tone, which provides the mathematical justification behind widely used statistical methods in a reader-friendly manner, and the book’s many examples, tutorials, exercises and problems for solution, together constitute an effective resource that students can read and learn from and instructors can count on as a worthy complement to their lectures. Using classroom-tested approaches that engage students in active learning, the text offers instructors the flexibility to control the mathematical level of their course. It contains the mathematical detail that is expected in a course for "majors" but is written in a way that emphasizes the intuitive content in statistical theory and the way theoretical results are used in practice. More than 1000 exercises and problems at varying levels of difficulty and with a broad range of topical focus give instructors many options in assigning homework and provide students with many problems on which to practice and from which to learn.

New Developments in Mining Engineering 2015

Author: Genadiy Pivnyak
Publisher: CRC Press
ISBN: 1315648776
Format: PDF, Kindle
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This annual series of books includes scientific papers on mining profiles. This volume presents multiple aspects of mining technology implementation in several aspects: extraction of coal, iron, manganese, uranium and other ores. Capturing and utilization of coalbed methane by various methods including alternative ones, safety measures in mining, ecological aspects, etc. Specific attention is paid to intensification of mineral resources extraction processes by way of modernizing opening methods, development and mining methods depending on mining-geological conditions. Experimental results of stress-strain state rock massif forecast by means of computational experiments using recursive methods are also discussed. Any mining operations should finally result in adequate recovery of land surface and utilization of mining wastes using various environmentally friendly methods, thus, sufficient attention is paid to this scientific trend. Non-traditional methods of minerals mining are becoming more topical and of higher demand in the modern society. Hence, several papers/chapters are devoted to underground coal gasification and its subsequent processes. In addition, extraction technologies of gas hydrate, as a source of an abundant amount of natural gas, are thoroughly examined in this book, including implementation of gas hydrate technologies for mine methane utilizations with its following transportation in a solid state. Furthermore, attention is given to evaluation of economic efficiency of minerals mining by the proposed methods, their ways of enrichment, ecological aspects and the influence of mining production on the environment, innovational logistic solutions at mining enterprises, and also to perspectives of Ukraine’s mining industry integration to the European standards.

Principles and Theory for Data Mining and Machine Learning

Author: Bertrand Clarke
Publisher: Springer Science & Business Media
ISBN: 0387981357
Format: PDF, Docs
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Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering

Discrete Simulation and Animation for Mining Engineers

Author: John R. Sturgul
Publisher: CRC Press
ISBN: 1482254425
Format: PDF, Mobi
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General Purpose Simulation System (GPSS) is a special computer programming language primarily used to simulate what can be classified as discrete systems. A discrete system is one where, at any given instant in time, a countable number of things can take place. The basic operation of a mine itself can be considered such a system. Discrete Simulation and Animation for Mining Engineers explains how to model mining systems using GPSS/H® and PROOF® by Wolverine Software Corporation. Employing a unique approach that encourages engagement from the start, the text discusses animation first, and then slowly introduces simulation language. As each new topic is covered, an animation is provided to illustrate the key concepts. Leveraging valuable insight gained from the author’s extensive experience modeling mines around the world, the book: Describes how to apply discrete system simulation to mines Shows how to make those simulations come alive with animation Includes real-world examples and exercises that hone practical problem-solving skills Written by a mining engineer for mining engineers and students of mining, Discrete Simulation and Animation for Mining Engineers offers a comprehensive yet accessible treatment of mine simulation and animation useful in increasing the efficiency of industrial mining processes.

Applied Data Mining

Author: Paolo Giudici
Publisher: John Wiley & Sons
ISBN: 0470871393
Format: PDF, ePub, Mobi
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Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance. This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems. Provides a solid introduction to applied data mining methods in a consistent statistical framework Includes coverage of classical, multivariate and Bayesian statistical methodology Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real life applications Features a number of detailed case studies based on applied projects within industry Incorporates discussion on software used in data mining, with particular emphasis on SAS Supported by a website featuring data sets, software and additional material Includes an extensive bibliography and pointers to further reading within the text Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.

Statistical and Machine Learning Data Mining

Author: Bruce Ratner
Publisher: CRC Press
ISBN: 1351652389
Format: PDF, Docs
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The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. is a compilation of new and creative data mining techniques, which address the scaling-up of the framework of classical and modern statistical methodology, for predictive modeling and analysis of big data. SM-DM provides proper solutions to common problems facing the newly minted data scientist in the data mining discipline. Its presentation focuses on the needs of the data scientists (commonly known as statisticians, data miners and data analysts), delivering practical yet powerful, simple yet insightful quantitative techniques, most of which use the "old" statistical methodologies improved upon by the new machine learning influence.

The Elements of Statistical Learning

Author: Trevor Hastie
Publisher: Springer Science & Business Media
ISBN: 0387216065
Format: PDF
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.