The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicabi...
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ISBN:
(数字)9783031316364
ISBN:
(纸本)9783031316357;9783031316388
The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail.;As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.
Im Mittelpunkt dieses;steht eine Einführung in ein bekanntes statistisches Modell, das *** können Probleme bewältigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustand...
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ISBN:
(数字)9783662659687
ISBN:
(纸本)9783662659670
Im Mittelpunkt dieses;steht eine Einführung in ein bekanntes statistisches Modell, das *** können Probleme bewältigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustandsspezifische Beschreibung geschlossen werden soll.;Die Anwendungen des Hidden-Markov-Modells liegen hauptsächlich in den Bereichen Bioinformatik, Computerlinguistik, maschinelles Lernen und Signalverarbeitung.;In diesem Büchlein werden die beiden zentralen Problemstellungen in HMMs behandelt.;Das Problem der Inferenz wird mit dem berühmten Viterbi-Algorithmus gelöst, und das Problem der Parameterschätzung wird mit zwei bekannten Methoden angegangen (Erwartungsmaximierung und Baum-Welch).
Privacy in statistical databases is a discipline whose purpose is to provide solutions to the tension between the increasing social, political and economical demand of accurate information, and the legal and ethical o...
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ISBN:
(数字)9783540874713
ISBN:
(纸本)9783540874706
Privacy in statistical databases is a discipline whose purpose is to provide solutions to the tension between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data owners (those organizations spending money in data collection) and the users (the ones querying the database, who would like their queries to stay con?d- tial). Beyond law and ethics, there are also practical reasons for data collecting agencies to invest in respondent privacy: if individual respondents feel their p- vacyguaranteed,they arelikelyto providemoreaccurateresponses. Data owner privacy is primarily motivated by practical considerations: if an enterprise c- lects data at its own expense, it may wish to minimize leakage of those data to other enterprises (even to those with whom joint data exploitation is planned). Finally, user privacy results in increased user satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy,bothofwhich started in the 1970s: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originatesfrom computer science and database technology. In o?cial statistics, the basic c- cern is respondent privacy.
As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regres...
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ISBN:
(数字)9783319194257
ISBN:
(纸本)9783319194240;9783319330396
As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.
Das Buch bietet eine Einführung in Wahrscheinlichkeitsmodelle und die Schätzung solcher Modelle auf der Grundlage von Beobachtungen. Neben klassischen statistischen Schätz- und Testverfahren und Regress...
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ISBN:
(数字)9783709133194
Das Buch bietet eine Einführung in Wahrscheinlichkeitsmodelle und die Schätzung solcher Modelle auf der Grundlage von Beobachtungen. Neben klassischen statistischen Schätz- und Testverfahren und Regressionsmodellen wird der Bayesschen Statistik gebührender Raum gewidmet. Ziel des Buches ist es, eine Einführung in die stochastische Beschreibung realer Phänomene zu geben, wobei die statistische Schätzung solcher Modelle auf der Grundlage von Daten entsprechend behandelt wird. Neu ist die Erweiterung der statistischen Schätzmethoden auf den Fall von in der Praxis oft vorkommenden nicht exakten Daten: dadurch, sowie durch die Aufnahme der Bayesschen Statistik, unterscheidet sich das vorliegende Werk grundlegend von vergleichbaren Büchern im deutschen Sprachraum.
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