Foundations Of The Prediction Process

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Foundations Of The Prediction Process

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Author : Frank B. Knight
language : en
Publisher:
Release Date : 1992

Foundations Of The Prediction Process written by Frank B. Knight and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Mathematics categories.

This book presents a unified treatment of the prediction process approach to continuous time stochastic processes. The underling idea is that there are two kinds of time: stationary physical time and the moving observer's time. By developing this theme, the author develops a theory of stochastic processes whereby two processes are considered which coexist on the same probability space. In this way, the observer' process is strongly Markovian. Consequently, any measurable stochastic process of a real parameter may be regarded as a homogeneous strong Markov process in an appropriate setting. This leads to a unifying principle for the representation of general processes in terms of martingales which facilitates the prediction of their properties. While the ideas are advanced, the methods are reasonable elementary and should be accessible to readers with basic knowledge of measure theory, functional analysis, stochastic integration, and probability on the level of the convergence theorem for positive super-martingales.

Foundations Of Time Series Analysis And Prediction Theory

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Author : Mohsen Pourahmadi
language : en
Publisher: John Wiley & Sons
Release Date : 2001-06-01

Foundations Of Time Series Analysis And Prediction Theory written by Mohsen Pourahmadi 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 2001-06-01 with Mathematics categories.

Foundations of time series for researchers and students This volume provides a mathematical foundation for time seriesanalysis and prediction theory using the idea of regression and thegeometry of Hilbert spaces. It presents an overview of the tools oftime series data analysis, a detailed structural analysis ofstationary processes through various reparameterizations employingtechniques from prediction theory, digital signal processing, andlinear algebra. The author emphasizes the foundation and structureof time series and backs up this coverage with theory andapplication. End-of-chapter exercises provide reinforcement for self-study andappendices covering multivariate distributions and Bayesianforecasting add useful reference material. Further coveragefeatures: * Similarities between time series analysis and longitudinal dataanalysis * Parsimonious modeling of covariance matrices through ARMA-likemodels * Fundamental roles of the Wold decomposition andorthogonalization * Applications in digital signal processing and Kalmanfiltering * Review of functional and harmonic analysis and predictiontheory Foundations of Time Series Analysis and Prediction Theory guidesreaders from the very applied principles of time series analysisthrough the most theoretical underpinnings of prediction theory. Itprovides a firm foundation for a widely applicable subject forstudents, researchers, and professionals in diverse scientificfields.

Metrics For Process Models

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Author : Jan Mendling
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-20

Metrics For Process Models written by Jan Mendling 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 2008-10-20 with Computers categories.

Business process modeling plays an important role in the management of business processes. As valuable design artifacts, business process models are subject to quality considerations. The absence of formal errors such as deadlocks is of paramount importance for the subsequent implementation of the process. In his book Jan Mendling develops a framework for the detection of formal errors in business process models and the prediction of error probability based on quality attributes of these models (metrics). He presents a precise description of Event-driven Process Chains (EPCs), their control-flow semantics and a suitable correctness criterion called EPC soundness.

Metrics For Process Models

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Author :
language : en
Publisher: Springer
Release Date : 2009-08-29

Metrics For Process Models written by and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-29 with Computers categories.

Business process modeling plays an important role in the management of business processes. As valuable design artifacts, business process models are subject to quality considerations. The absence of formal errors such as deadlocks is of paramount importance for the subsequent implementation of the process. In his book Jan Mendling develops a framework for the detection of formal errors in business process models and the prediction of error probability based on quality attributes of these models (metrics). He presents a precise description of Event-driven Process Chains (EPCs), their control-flow semantics and a suitable correctness criterion called EPC soundness.

Experience And Prediction

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Author : Hans Reichenbach
language : en
Publisher:
Release Date : 2006

Experience And Prediction written by Hans Reichenbach and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Philosophy categories.

Hans Reichenbach was a formidable figure in early 20th century philosophy of science. In the two decades following World War II, logical positivism was the reigning theory of the philosophy of science and Reichenbach was at the peak of his career. He wrote this book to introduce logical positivism to English speakers.

Applied Predictive Modeling

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Author : Max Kuhn
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-05-17

Applied Predictive Modeling written by Max Kuhn 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 2013-05-17 with Medical categories.

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.

Foundations Of The Theory Of Prediction

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Author : Harry Cranbrook Allen
language : en
Publisher:
Release Date : 1966

Foundations Of The Theory Of Prediction written by Harry Cranbrook Allen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1966 with categories.

Foundations Of Complex Systems

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Author : Gregoire Nicolis
language : en
Publisher: World Scientific
Release Date : 2007

Foundations Of Complex Systems written by Gregoire Nicolis 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 Science categories.

A geometric process is a simple monotone process that was first introduced by the author in 1988. It is a generalization of renewal process. This book captures the extensive research work on geometric processes that has been done since then in both probability and statistics theory and various applications. Some results are published for the first time. A reference book for researchers and a handbook for practioners, it is also a useful textbook for postgraduate or senior undergraduate students.

Foundations Of Data Science

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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23

Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Practical Guide To Applied Conformal Prediction In Python

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Author : Valery Manokhin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-20

Practical Guide To Applied Conformal Prediction In Python written by Valery Manokhin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-20 with Mathematics categories.

Elevate your machine learning skills using the Conformal Prediction framework for uncertainty quantification. Dive into unique strategies, overcome real-world challenges, and become confident and precise with forecasting. Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications Explore cutting-edge methods to measure and manage uncertainty in industry applications Understand how Conformal Prediction differs from traditional machine learning Book DescriptionIn the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.