| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
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| E-Books | Biblioteca da FCTUNL Online | Não Ficção | QA273.6.SPR FCT 81028 (Browse shelf) | 1 | Available |
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| QA273.6.SPR FCT 102545 Methods for the analysis of asymmetric proximity data | QA273.6.SPR FCT 80791 Continuous bivariate distributions | QA273.6.SPR FCT 80935 Comparing distributions | QA273.6.SPR FCT 81028 Probability and statistical models | QA273.6.SPR FCT 81241 An introduction to heavy-tailed and subexponential distributions | QA273.6.SPR FCT 81353 Applications of discrete-time Markov chains and poisson processes to air pollution modeling and studies | QA273.6.SPR FCT 81453 Stochastic orders in reliability and risk |
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With an emphasis on models and techniques, this textbook introduces many of the fundamental concepts of stochastic modeling that are now a vital component of almost every scientific investigation. These models form the basis of well-known parametric lifetime distributions such as exponential, Weibull, and gamma distributions, as well as change-point and mixture models. The authors also consider more general notions of non-parametric lifetime distribution classes. In particular, emphasis is placed on laying the foundation for solving problems in reliability, insurance, finance, and credit risk. Exercises and solutions to selected problems accompany each chapter in order to allow students to explore these foundations. The key subjects covered include: * Exponential distributions and the Poisson process * Parametric lifetime distributions * Non-parametric lifetime distribution classes * Multivariate exponential extensions * Association and dependence * Renewal theory * Problems in reliability, insurance, finance, and credit risk This work differs from traditional probability textbooks in a number of ways. Since no measure theory knowledge is necessary to understand the material and coverage of the central limit theorem and normal theory related topics has been omitted, the work may be used as a single-semester senior undergraduate or first-year graduate textbook as well as in a second course on probability modeling. Many of the chapters that examine central topics in applied probability can be read independently, allowing both instructors and readers extra flexibility in their use of the book. Probability and Statistical Models is for a wide audience including advanced undergraduate and beginning-level graduate students, researchers, and practitioners in mathematics, statistics, engineering, and economics.
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