Recent Advances In Stochastic Modeling And Data Analysis
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Recent Advances In Stochastic Modeling And Data Analysis
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Author : Christos H Skiadas
language : en
Publisher: World Scientific
Release Date : 2007-11-16
Recent Advances In Stochastic Modeling And Data Analysis written by Christos H Skiadas and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-16 with Mathematics categories.
This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues, survival analysis, and time series. The volume presents new results with potential for solving real-life problems and provides novel methods for solving these problems by analyzing the relevant data. The use of recent advances in different fields is emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural computing, and bioinformatics.
Advances In Stochastic Modelling And Data Analysis
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Author : Jacques Janssen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Advances In Stochastic Modelling And Data Analysis written by Jacques Janssen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Mathematics categories.
Advances in Stochastic Modelling and Data Analysis presents the most recent developments in the field, together with their applications, mainly in the areas of insurance, finance, forecasting and marketing. In addition, the possible interactions between data analysis, artificial intelligence, decision support systems and multicriteria analysis are examined by top researchers. Audience: A wide readership drawn from theoretical and applied mathematicians, such as operations researchers, management scientists, statisticians, computer scientists, bankers, marketing managers, forecasters, and scientific societies such as EURO and TIMS.
Recent Advances In Stochastic Operations Research
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Author : Tadashi Dohi
language : en
Publisher: World Scientific
Release Date : 2007
Recent Advances In Stochastic Operations Research written by Tadashi Dohi and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Business & Economics categories.
Operations research uses quantitative models to analyze and predict the behavior of systems and to provide information for decision makers. Two key concepts in operations research are optimization and uncertainty. This volume consists of a collection of peer reviewed papers from the International Workshop on Recent Advances in Stochastic Operations Research (RASOR 2005), August 25OCo26, 2005, Canmore, Alberta, Canada. In particular, the book focusses on models in stochastic operations research, including queueing models, inventory models, financial engineering models, reliability models, and simulations models."
Recent Advances In Stochastic Modeling And Data Analysis
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Author : Christos H. Skiadas
language : en
Publisher: World Scientific
Release Date : 2007
Recent Advances In Stochastic Modeling And Data Analysis written by Christos H. Skiadas and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Mathematics categories.
This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues, survival analysis, and time series. The volume presents new results with potential for solving real-life problems and provides novel methods for solving these problems by analyzing the relevant data. The use of recent advances in different fields is emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural computing, and bioinformatics. Sample Chapter(s). Chapter 1: An approach to Stochastic Process using Quasi-Arithmetic Means (373 KB). Contents: Stochastic Processes and Models; Distributions; Insurance; Stochastic Modeling for Healthcare Management; Markov and Semi Markov Models; Parametric/Non-Parametric; Dynamical Systems/Forecasting; Modeling and Stochastic Modeling; Statistical Applications in Socioeconimic Problems; Sampling and Optimization Problems; Data Mining and Applications; Clustering and Classification; Applications of Data Analysis; Miscellaneous. Readership: Researchers in probability and statistics, stochastics and fuzzy logic.
Applied Modeling Techniques And Data Analysis 2
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Author : Yannis Dimotikalis
language : en
Publisher: John Wiley & Sons
Release Date : 2021-03-26
Applied Modeling Techniques And Data Analysis 2 written by Yannis Dimotikalis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Business & Economics categories.
BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Recent Developments In Stochastic Methods And Applications
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Author : Albert N. Shiryaev
language : en
Publisher: Springer Nature
Release Date : 2021-08-02
Recent Developments In Stochastic Methods And Applications written by Albert N. Shiryaev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-02 with Mathematics categories.
Highlighting the latest advances in stochastic analysis and its applications, this volume collects carefully selected and peer-reviewed papers from the 5th International Conference on Stochastic Methods (ICSM-5), held in Moscow, Russia, November 23-27, 2020. The contributions deal with diverse topics such as stochastic analysis, stochastic methods in computer science, analytical modeling, asymptotic methods and limit theorems, Markov processes, martingales, insurance and financial mathematics, queueing theory and stochastic networks, reliability theory, risk analysis, statistical methods and applications, machine learning and data analysis. The 29 articles in this volume are a representative sample of the 87 high-quality papers accepted and presented during the conference. The aim of the ICSM-5 conference is to promote the collaboration of researchers from Russia and all over the world, and to contribute to the development of the field of stochastic analysis and applications of stochastic models.
Selected Topics On Stochastic Modelling
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Author : Mariano J Valderrama Bonnet
language : en
Publisher: World Scientific
Release Date : 1994-09-30
Selected Topics On Stochastic Modelling written by Mariano J Valderrama Bonnet and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-09-30 with categories.
This volume contains a selection of papers on recent developments in fields such as stochastic processes, multivariate data analysis and stochastic models in operations research, earth and life sciences and information theory, from an applicative perspective. Some of them have been extracted from lectures given at the Department of Statistics and Operations Research at the University of Granada for the past two years (Kai Lai Chung and Marcel F Neuts, among others). All the papers have been carefully selected and revised.
Stochastic Models Statistics And Their Applications
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Author : Ansgar Steland
language : en
Publisher: Springer
Release Date : 2015-02-04
Stochastic Models Statistics And Their Applications written by Ansgar Steland and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-04 with Mathematics categories.
This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.
Stochastic Models Statistics And Their Applications
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Author : Ansgar Steland
language : en
Publisher: Springer Nature
Release Date : 2019-10-15
Stochastic Models Statistics And Their Applications written by Ansgar Steland and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-15 with Mathematics categories.
This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.
The Data Science Handbook
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Author : Field Cady
language : en
Publisher: John Wiley & Sons
Release Date : 2017-02-28
The Data Science Handbook written by Field Cady and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-28 with Mathematics categories.
A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.