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Stochastic processes --- Processus stochastiques --- Markov renewal tehory --- 519.216 --- Random processes --- Probabilities --- Stochastic processes in general. Prediction theory. Stopping times. Martingales --- 519.216 Stochastic processes in general. Prediction theory. Stopping times. Martingales
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This text is an introduction to the modern theory and applications of probability and stochastics. The style and coverage is geared towards the theory of stochastic processes, but with some attention to the applications. In many instances the gist of the problem is introduced in practical, everyday language and then is made precise in mathematical form. The first four chapters are on probability theory: measure and integration, probability spaces, conditional expectations, and the classical limit theorems. There follows chapters on martingales, Poisson random measures, Levy Processes, Brownian motion, and Markov Processes. Special attention is paid to Poisson random measures and their roles in regulating the excursions of Brownian motion and the jumps of Levy and Markov processes. Each chapter has a large number of varied examples and exercises. The book is based on the author’s lecture notes in courses offered over the years at Princeton University. These courses attracted graduate students from engineering, economics, physics, computer sciences, and mathematics. Erhan Çinlar has received many awards for excellence in teaching, including the President’s Award for Distinguished Teaching at Princeton University. His research interests include theories of Markov processes, point processes, stochastic calculus, and stochastic flows. The book is full of insights and observations that only a lifetime researcher in probability can have, all told in a lucid yet precise style.
Electronic books. -- local. --- Probabilities. --- Stochastic analysis. --- Stochastic processes --- Probabilities --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Analysis, Stochastic --- Probability --- Statistical inference --- Mathematics. --- Measure theory. --- Probability Theory and Stochastic Processes. --- Measure and Integration. --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Mathematical analysis --- Distribution (Probability theory. --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Lebesgue measure --- Measurable sets --- Measure of a set --- Algebraic topology --- Integrals, Generalized --- Measure algebras --- Rings (Algebra)
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Operational research. Game theory --- Mathematical physics --- differentiaalvergelijkingen --- stochastische analyse --- kansrekening
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This book offers a first course in analysis for scientists and engineers. It can be used at the advanced undergraduate level or as part of the curriculum in a graduate program. The book is built around metric spaces. In the first three chapters, the authors lay the foundational material and cover the all-important “four-C’s”: convergence, completeness, compactness, and continuity. In subsequent chapters, the basic tools of analysis are used to give brief introductions to differential and integral equations, convex analysis, and measure theory. The treatment is modern and aesthetically pleasing. The book contains detailed illustrations, examples and exercises. It lays the groundwork for the needs of classical fields as well as the important new fields of optimization and probability theory.
Mathematics. --- Mathematical analysis. --- Analysis (Mathematics). --- Mathematical optimization. --- Operations research. --- Management science. --- Applied mathematics. --- Engineering mathematics. --- Analysis. --- Appl.Mathematics/Computational Methods of Engineering. --- Operations Research, Management Science. --- Optimization. --- Global analysis (Mathematics). --- Mathematical and Computational Engineering. --- Analysis, Global (Mathematics) --- Differential topology --- Functions of complex variables --- Geometry, Algebraic --- Engineering --- Engineering analysis --- Mathematical analysis --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Mathematics --- Functions of real variables --- Global analysis (Mathematics) --- Real variables --- Quantitative business analysis --- Management --- Problem solving --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- 517.1 Mathematical analysis
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This text is an introduction to the modern theory and applications of probability and stochastics. The style and coverage is geared towards the theory of stochastic processes, but with some attention to the applications. In many instances the gist of the problem is introduced in practical, everyday language and then is made precise in mathematical form. The first four chapters are on probability theory: measure and integration, probability spaces, conditional expectations, and the classical limit theorems. There follows chapters on martingales, Poisson random measures, Levy Processes, Brownian motion, and Markov Processes. Special attention is paid to Poisson random measures and their roles in regulating the excursions of Brownian motion and the jumps of Levy and Markov processes. Each chapter has a large number of varied examples and exercises. The book is based on the author's lecture notes in courses offered over the years at Princeton University. These courses attracted graduate students from engineering, economics, physics, computer sciences, and mathematics. Erhan Çinlar has received many awards for excellence in teaching, including the President's Award for Distinguished Teaching at Princeton University. His research interests include theories of Markov processes, point processes, stochastic calculus, and stochastic flows. The book is full of insights and observations that only a lifetime researcher in probability can have, all told in a lucid yet precise style.
Operational research. Game theory --- Mathematical physics --- differentiaalvergelijkingen --- stochastische analyse --- kansrekening
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This book offers a first course in analysis for scientists and engineers. It can be used at the advanced undergraduate level or as part of the curriculum in a graduate program. The book is built around metric spaces. In the first three chapters, the authors lay the foundational material and cover the all-important “four-C’s”: convergence, completeness, compactness, and continuity. In subsequent chapters, the basic tools of analysis are used to give brief introductions to differential and integral equations, convex analysis, and measure theory. The treatment is modern and aesthetically pleasing. The book contains detailed illustrations, examples and exercises. It lays the groundwork for the needs of classical fields as well as the important new fields of optimization and probability theory.
Differential geometry. Global analysis --- Mathematical analysis --- Numerical methods of optimisation --- Operational research. Game theory --- Mathematical statistics --- Mathematics --- Applied physical engineering --- Engineering sciences. Technology --- Planning (firm) --- Computer. Automation --- analyse (wiskunde) --- automatisering --- economie --- mathematische modellen --- statistiek --- econometrie --- wiskunde --- operationeel onderzoek --- ingenieurswetenschappen
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