Download or read book TRADITIONAL AND DATA DRIVEN PREDICTIVE STATISTICAL MODELS written by Dr. Neeta Kishor Dhane and published by Laxmi Book Publication. This book was released on 2021-07-23 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: The desire to know the unknown has always been one of the human characteristics that distinguish humans from other living things on the earth. The past is known but cannot be changed, and hence is if no interest. The present is happening and everyone is witnessing it and therefore it is not exciting. But the future is both unknown and perhaps therefore uncertain, and is therefore both interesting and exciting. Using past experience for predicting the unknown future was initially treated as an art because it require careful choice of parts of the past that will make prediction both easy and accurate, and there were times when it was felt that it is impossible to formulate a method for this. Prediction was then not considered to be scientific empirical sciences that learn from scientist and professionals realized the scientific nature of the ability to predict. What then began as the preparation for developing a prediction formula involved finding common patterns in past data and their consequences so that the consequence can be predicted as soon as the relevant pattern is observed. At the same time the discipline of statistics developed the concept and methodology for building statistical models. With experience in the development and applications of different models, scientists and researchers identify models as belonging to four different classes namely, the class of descriptive models, the class of diagnostic models, the class of predictive models, and the class of prescriptive or prognostic models. The scientific or theoretical activity of building models and analyzing data accordingly is known as analytics. It has therefore been recognized that there are four classes of analytics, namely descriptive analytics, diagnostic analytics, prescriptive analytics and predictive analytics. These four classes are defined briefly for convenience of the reader.

Download or read book Development of Data Driven Models for Chemical Engineering Systems written by Nusrat Parveen and published by Mohammed Abdul Malik. This book was released on 2024-03-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of any system or a process is one of the significant but challenging tasks in engineering. The challenge is either due to the physical complexity of natural phenomenon or our limited knowledge of mathematics. Recently, data driven modeling (DDM) has been found to be a very powerful tool in helping to overcome those challenges, by presenting opportunities to build basic models from the observed patterns as well as accelerating the response of decision makers in facing real world problems. Since DDM is able to map causal factors and consequent outcomes from the observed patterns (experimental data), without deep knowledge of the complex physical process, these modeling techniques are becoming popular among engineers. Soft computing and statistical models are the two commonly employed data-driven models for predictive modeling. As far as the statistical data-driven models are concerned, these models could be employed in the life of modern engineering. But the accuracy and generalizability of these models is very poor. The soft computing data- driven modeling techniques have attracted the attention of many researchers across the globe to overcome the limitations of statistical methods. The statistical data-driven modeling techniques such as least-squares methods, the maximum likelihood methods and traditional artificial neural network (ANN) are based on empirical risk minimization (ERM) principle while the support vector machine (SVM) method is based on the structural risk minimization (SRM) principle. According to it, the generalization accuracy is optimized over the empirical error and the flatness of the regression function or the capacity of SVM. On the other hand, the ANN and other traditional regression models which are based on ERM principle minimize the empirical error and do not consider the capacity of the learning machines. This results in model over fitting i.e. high prediction accuracy for the training data set and low for the test (unseen) data, giving poor generalization performance. SVMs belong to the supervised machine learning theory and are applied to both nonlinear classification called support vector classification (SVC) and regression or SVR. SVM possesses many advantages over traditional neural networks.

Download or read book Modern Statistics with R written by Måns Thulin and published by CRC Press. This book was released on 2024-08-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.

Download or read book Statistical and Econometric Methods for Transportation Data Analysis written by Simon Washington and published by CRC Press. This book was released on 2020-01-30 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.

Download or read book Data Driven Mathematical and Statistical Models of Online Social Networks written by Shudong Li and published by Frontiers Media SA. This book was released on 2022-03-07 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Driven Optimization and Knowledge Discovery for an Enterprise Information System written by Qing Duan and published by Springer. This book was released on 2015-06-13 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.

Download or read book APPLICATION OF DECISION TREE FOR DEVELOPING ACCURATE PREDICTION MODELS written by Dr. Pratibha Vijay Jadhav & Dr. Vaishali Vilas Patil and published by Ashok Yakkaldevi. This book was released on 2022-06-22 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today’s world is bounded by data, from morning to night each and all work is associated to data. The usage of computer and its technology is rapidly growing in many different fields like Education, banking sector, bioinformatics field, business, health cares and Industry. In all ways, everywhere data is created and this information is stored in various hubs or data wares houses. There is huge amount of data and it is created by increasing usage of computer. There is rapidly growth of data generated by all systems and it can be used for deriving models by assessing useful relationship among input and output dependencies. Consequently, there is presently shifted a model since classical modelling and it investigates to develop a model and the equivalent analyses from stored data. Government organizations, scientific institutions, administration offices and businesses have all dedicated huge resources to assembly and putting away information. Now a days, Data can possibly assist organizations with improving tasks and make quicker, progressively powerful decisions. The information or data is gathered from various sources including messages, cell phones, applications, databases, servers and different methods. This information is collected, arranged, controlled and put in meaningful information. This meaningful information would assist to an organization with valuable understanding to hold the clients for expand the income and improved the business activities. The government organizations and companies are gathering the useful information to support to manage human resources.

Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Download or read book Predictive Modeling and Analytics written by Jeffrey Strickland and published by Lulu.com. This book was released on 2014-08-06 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)-this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no "by-hand" exercises).

Download or read book Physically Inspired Methods and Development of Data driven Predictive Systems written by Marcin Budka and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditionally building of predictive models is perceived as a combination of both science and art. Although the designer of a predictive system effectively follows a prescribed procedure, his domain knowledge as well as expertise and intuition in the field of machine learning are often irreplaceable. However, in many practical situations it is possible to build well?performing predictive systems by following a rigorous methodology and offsetting not only the lack of domain knowledge but also partial lack of expertise and intuition, by computational power. The generalised predictive model development cycle discussed in this thesis is an example of such methodology, which despite being computationally expensive, has been successfully applied to real?world problems. The proposed predictive system design cycle is a purely data?driven approach. The quality of data used to build the system is thus of crucial importance. In practice however, the data is rarely perfect. Common problems include missing values, high dimensionality or very limited amount of labelled exemplars. In order to address these issues, this work investigated and exploited inspirations coming from physics. The novel use of well?established physical models in the form of potential fields, has resulted in derivation of a comprehensive Electrostatic Field Classification Framework for supervised and semi?supervised learning from incomplete data. Although the computational power constantly becomes cheaper and more accessible, it is not infinite. Therefore efficient techniques able to exploit finite amount of predictive information content of the data and limit the computational requirements of the resource?hungry predictive system design procedure are very desirable. In designing such techniques this work once again investigated and exploited inspirations coming from physics. By using an analogy with a set of interacting particles and the resulting Information Theoretic Learning framework, the Density Preserving Sampling technique has been derived. This technique acts as a computationally efficient alternative for cross?validation, which fits well within the proposed methodology. All methods derived in this thesis have been thoroughly tested on a number of benchmark datasets. The proposed generalised predictive model design cycle has been successfully applied to two real?world environmental problems, in which a comparative study of Density Preserving Sampling and cross?validation has also been performed confirming great potential of the proposed methods.

Download or read book Translational Systems Biology written by Yoram Vodovotz and published by Elsevier. This book was released on 2014-10-08 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are we satisfied with the rate of drug development? Are we happy with the drugs that come to market? Are we getting our money's worth in spending for basic biomedical research? In Translational Systems Biology, Drs. Yoram Vodovotz and Gary An address these questions by providing a foundational description the barriers facing biomedical research today and the immediate future, and how these barriers could be overcome through the adoption of a robust and scalable approach that will form the underpinning of biomedical research for the future. By using a combination of essays providing the intellectual basis of the Translational Dilemma and reports of examples in the study of inflammation, the content of Translational Systems Biology will remain relevant as technology and knowledge advances bring broad translational applicability to other diseases. Translational systems biology is an integrated, multi-scale, evidence-based approach that combines laboratory, clinical and computational methods with an explicit goal of developing effective means of control of biological processes for improving human health and rapid clinical application. This comprehensive approach to date has been utilized for in silico studies of sepsis, trauma, hemorrhage, and traumatic brain injury, acute liver failure, wound healing, and inflammation. - Provides an explicit, reasoned, and systematic approach to dealing with the challenges of translational science across disciplines - Establishes the case for including computational modeling at all stages of biomedical research and healthcare delivery, from early pre-clinical studies to long-term care, by clearly delineating efficiency and costs saving important to business investment - Guides readers on how to communicate across domains and disciplines, particularly between biologists and computational researchers, to effectively develop multi- and trans-disciplinary research teams

Download or read book Risk Analysis Based on Data and Crisis Response Beyond Knowledge written by Chongfu Huang and published by CRC Press. This book was released on 2019-10-11 with total page 958 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects the papers presented at the 7th International Conference on Risk Analysis and Crisis Response (RACR-2019) held in Athens, Greece, on October 15-19, 2019. The overall theme of the seventh international conference on risk analysis and crisis response is Risk Analysis Based on Data and Crisis Response Beyond Knowledge, highlighting science and technology to improve risk analysis capabilities and to optimize crisis response strategy. This book contains primarily research articles of risk issues. Underlying topics include natural hazards and major (chemical) accidents prevention, disaster risk reduction and society resilience, information and communication technologies safety and cybersecurity, modern trends in crisis management, energy and resources security, critical infrastructure, nanotechnology safety and others. All topics include aspects of multidisciplinarity and complexity of safety in education and research. The book should be valuable to professors, engineers, officials, businessmen and graduate students in risk analysis and risk management.

Download or read book Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Download or read book Machine Learning and Probabilistic Graphical Models for Decision Support Systems written by Kim Phuc Tran and published by CRC Press. This book was released on 2022-10-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.

Download or read book Statistical Prediction and Machine Learning written by John Tuhao Chen and published by CRC Press. This book was released on 2024-08-06 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources. One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors’ teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods. Key Features: Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science. Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy. Integrates statistical theory with machine learning algorithms. Includes potential methodological developments in data science.

Download or read book Handbook of Travel Behaviour written by Dimitris Potoglou and published by Edward Elgar Publishing. This book was released on 2024-04-12 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: This insightful Handbook offers a comprehensive and diverse understanding of the determinants of travel behaviour, looking at the ways in which it can be better understood, modelled and forecasted. Dimitris Potoglou and Justin Spinney bring together an international range of esteemed academics who explore the origins of the field, research analysis methods, environmental considerations, and social factors. This title contains one or more Open Access chapters.

Download or read book Neural Information Processing written by Biao Luo and published by Springer Nature. This book was released on 2023-11-14 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.