Some Problems In Prediction Theory
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Some Problems in Prediction Theory
Author | : Glen Meeden |
Publisher | : |
Total Pages | : 170 |
Release | : 1968 |
Genre | : Estimation theory |
ISBN | : |
Prediction, Learning, and Games
Author | : Nicolo Cesa-Bianchi |
Publisher | : Cambridge University Press |
Total Pages | : 4 |
Release | : 2006-03-13 |
Genre | : Computers |
ISBN | : 113945482X |
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
The Theory of Linear Prediction
Author | : P. Vaidyanathan |
Publisher | : Springer Nature |
Total Pages | : 183 |
Release | : 2022-06-01 |
Genre | : Technology & Engineering |
ISBN | : 303102527X |
Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signal processing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter. Table of Contents: Introduction / The Optimal Linear Prediction Problem / Levinson's Recursion / Lattice Structures for Linear Prediction / Autoregressive Modeling / Prediction Error Bound and Spectral Flatness / Line Spectral Processes / Linear Prediction Theory for Vector Processes / Appendix A: Linear Estimation of Random Variables / B: Proof of a Property of Autocorrelations / C: Stability of the Inverse Filter / Recursion Satisfied by AR Autocorrelations
Evolutionary Cognitive Neuroscience
Author | : Steven Platek |
Publisher | : MIT Press |
Total Pages | : 637 |
Release | : 2007 |
Genre | : Medical |
ISBN | : 0262162415 |
An essential reference for the new discipline of evolutionary cognitive neuroscience that defines the field's approach of applying evolutionary theory to guide brain-behavior investigations. Since Darwin we have known that evolution has shaped all organisms and that biological organs—including the brain and the highly crafted animal nervous system—are subject to the pressures of natural and sexual selection. It is only relatively recently, however, that the cognitive neurosciences have begun to apply evolutionary theory and methods to the study of brain and behavior. This landmark reference documents and defines the emerging field of evolutionary cognitive neuroscience. Chapters by leading researchers demonstrate the power of the evolutionary perspective to yield new data, theory, and insights on the evolution and functional modularity of the brain. Evolutionary cognitive neuroscience covers all areas of cognitive neuroscience, from nonhuman brain-behavior relationships to human cognition and consciousness, and each section of Evolutionary Cognitive Neuroscience addresses a different adaptive problem. After an introductory section that outlines the basic tenets of both theory and methodology of an evolutionarily informed cognitive neuroscience, the book treats neuroanatomy from ontogenetic and phylogenetic perspectives and explores reproduction and kin recognition, spatial cognition and language, and self-awareness and social cognition. Notable findings include a theory to explain the extended ontogenetic and brain development periods of big-brained organisms, fMRI research on the neural correlates of romantic attraction, an evolutionary view of sex differences in spatial cognition, a theory of language evolution that draws on recent research on mirror neurons, and evidence for a rudimentary theory of mind in nonhuman primates. A final section discusses the ethical implications of evolutionary cognitive neuroscience and the future of the field. Contributors: C. Davison Ankney, Simon Baron-Cohen, S. Marc Breedlove, William Christiana, Michael Corballis, Robin I. M. Dunbar, Russell Fernald, Helen Fisher, Jonathan Flombaum, Farah Focquaert, Steven J.C. Gaulin, Aaron Goetz, Kevin Guise, Ruben C. Gur, William D. Hopkins, Farzin Irani, Julian Paul Keenan, Michael Kimberly, Stephen Kosslyn, Sarah L. Levin, Lori Marino, David Newlin, Ivan S. Panyavin, Shilpa Patel, Webb Phillips, Steven M. Platek, David Andrew Puts, Katie Rodak, J. Philippe Rushton, Laurie Santos, Todd K. Shackelford, Kyra Singh, Sean T. Stevens, Valerie Stone, Jaime W. Thomson, Gina Volshteyn, Paul Root Wolpe
On Some Problems in the Theory of Perfect Prediction
Author | : Joseph Francis Anthony Ormsby |
Publisher | : |
Total Pages | : 300 |
Release | : 1963 |
Genre | : |
ISBN | : |
Conformal Prediction for Reliable Machine Learning
Author | : Vineeth Balasubramanian |
Publisher | : Newnes |
Total Pages | : 323 |
Release | : 2014-04-23 |
Genre | : Computers |
ISBN | : 0124017150 |
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Interpretable Machine Learning
Author | : Christoph Molnar |
Publisher | : Lulu.com |
Total Pages | : 320 |
Release | : 2020 |
Genre | : Artificial intelligence |
ISBN | : 0244768528 |
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Linear Prediction Theory
Author | : Peter Strobach |
Publisher | : Springer Science & Business Media |
Total Pages | : 434 |
Release | : 2012-12-06 |
Genre | : Science |
ISBN | : 3642752063 |
Lnear prediction theory and the related algorithms have matured to the point where they now form an integral part of many real-world adaptive systems. When it is necessary to extract information from a random process, we are frequently faced with the problem of analyzing and solving special systems of linear equations. In the general case these systems are overdetermined and may be characterized by additional properties, such as update and shift-invariance properties. Usually, one employs exact or approximate least-squares methods to solve the resulting class of linear equations. Mainly during the last decade, researchers in various fields have contributed techniques and nomenclature for this type of least-squares problem. This body of methods now constitutes what we call the theory of linear prediction. The immense interest that it has aroused clearly emerges from recent advances in processor technology, which provide the means to implement linear prediction algorithms, and to operate them in real time. The practical effect is the occurrence of a new class of high-performance adaptive systems for control, communications and system identification applications. This monograph presumes a background in discrete-time digital signal processing, including Z-transforms, and a basic knowledge of discrete-time random processes. One of the difficulties I have en countered while writing this book is that many engineers and computer scientists lack knowledge of fundamental mathematics and geometry.
Foundations of Time Series Analysis and Prediction Theory
Author | : Mohsen Pourahmadi |
Publisher | : John Wiley & Sons |
Total Pages | : 446 |
Release | : 2001-06-01 |
Genre | : Mathematics |
ISBN | : 9780471394341 |
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.