Probability and Stochastic Modeling

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A01=Vladimir I. Rotar
advanced risk assessment models
Author_Vladimir I. Rotar
Berry Esseen Theorem
Binomial Tree
birth-death process analysis
Birth-Death Processes
Brownian Motion
Category=PBT
Category=PBWL
Central Limit Theorem
Certainty Equivalent
Conditional Expectation
Cumulative Distribution Function
dependency structure classification
Dependency Structures
Distribution Function
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
EU Maximizer
Event A1
Exercise Price
Financial Market Model
Homogeneous Poisson Process
Independent Increments
Interarrival Time
Introductory Probability
Markov chain modeling
Markov Chains
Martingales
Memoryless Property
Modern Tendencies In Probability Theory
Negative Binomial Distribution
Probability And Stochastic Modeling Textbook
Process Xt
Process Yt
reliability engineering
Reliability Models
Risk Evaluation
Risk Free Asset
Risk Neutral World
Ruin Probability
Stationary Regime
stochastic simulation techniques
survival probability methods
Symmetric Random Walk
Vice Versa
Wiener Process

Product details

  • ISBN 9780367380946
  • Weight: 861g
  • Dimensions: 178 x 254mm
  • Publication Date: 23 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Probability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability texts, the book also focuses on increasingly important areas, such as martingales, classification of dependency structures, and risk evaluation. Numerous examples, exercises, and models using real-world data demonstrate the practical possibilities and restrictions of different approaches and help students grasp general concepts and theoretical results. The text is suitable for majors in mathematics and statistics as well as majors in computer science, economics, finance, and physics. The author offers two explicit options to teaching the material, which is reflected in "routes" designated by special "roadside" markers. The first route contains basic, self-contained material for a one-semester course. The second provides a more complete exposition for a two-semester course or self-study.

Vladimir I. Rotar is a professor in the Department of Mathematics and Statistics at San Diego State University. Dr. Rotar has authored four books and more than 100 scientific papers on probability theory and its applications in leading mathematical journals.

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