LEADER 05799nam 2200781 a 450 001 9910139240403321 005 20200520144314.0 010 $a9781118623527 010 $a1118623525 010 $a9781118623220 010 $a1118623223 010 $a9781299315655 010 $a1299315658 010 $a9780470394076 010 $a0470394072 035 $a(CKB)2560000000100646 035 $a(EBL)700699 035 $a(SSID)ssj0000835624 035 $a(PQKBManifestationID)11474302 035 $a(PQKBTitleCode)TC0000835624 035 $a(PQKBWorkID)11008457 035 $a(PQKB)11364044 035 $a(MiAaPQ)EBC700699 035 $a(CaSebORM)9781118623527 035 $a(OCoLC)830627895 035 $a(PPN)190746297 035 $a(OCoLC)876268751 035 $a(OCoLC)ocn876268751 035 $a(Perlego)1006081 035 $a(EXLCZ)992560000000100646 100 $a20091231d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to stochastic models /$fMarius Iosifescu, Nikolaos Limnios, Gheorghe Oprisan ; series editor, Nikolaos Limnios 205 $a1st edition 210 $aLondon $cISTE ;$aHoboken, N.J. $cWiley$d2010 215 $a1 online resource (385 p.) 225 1 $aApplied stochastic methods series 300 $a"First published 2007 in France by Hermes Science/Lavoisier entitled: Modeles stochastiques"--t.p. verso. 311 08$a9781848210578 311 08$a1848210574 320 $aIncludes bibliographical references and index. 327 $aCover; Introduction to Stochastic Models; Title Page; Copyright Page; Table of Contents; Preface; Chapter 1. Introduction to Stochastic Processes; 1.1. Sequences of random variables; 1.2. The notion of stochastic process; 1.3. Martingales; 1.3.1. Stopping time; 1.3.2. Discrete-time martingales; 1.3.3. Martingale convergence; 1.3.4. Square integrable martingales; 1.4. Markov chains; 1.4.1. Markov property; 1.4.2. Transition function; 1.4.3. Strong Markov property; 1.5. State classification; 1.5.1. Stationary probability; 1.6. Continuous-time Markov processes; 1.6.1. Transition function 327 $a1.6.2. Kolmogorov equations1.7. Semi-Markov processes; 1.7.1. Markov renewal processes; 1.7.2. Semi-Markov processes; Chapter 2. Simple Stochastic Models; 2.1. Urn models; 2.2. Random walks; 2.3. Brownian motion; 2.3.1. Introduction; 2.3.2. Basic properties; 2.4. Poisson processes; 2.5. Birth and death processes; Chapter 3. Elements of Markov Modeling; 3.1. Markov models: ideas, history, applications; 3.2. The discrete-time Ehrenfest model; 3.2.1. The microscopic chain; 3.2.2. The macroscopic chain; 3.2.3. Some characteristics of the Ehrenfest model 327 $a3.2.4. The discrete-time Ehrenfest model: history, generalizations, similar models3.3. Markov models in genetics; 3.3.1. Laws of heredity and mathematics; 3.3.2. Haploid models; 3.3.3. Models with two genotypes and without mutations; 3.3.4. Models with several genotypes and without mutations; 3.3.5. Models with two genotypes and mutations; 3.3.6. Models with several genotypes and mutations; 3.3.7. Models with partitioned population; 3.3.8. Genealogy models for large size populations; 3.4. Markov storage models; 3.4.1. Discrete-time models; 3.4.2. Continuous-time models 327 $a3.4.3. A generalized storage model3.5. Reliability of Markov models; 3.5.1. Introduction to reliability; 3.5.2. Some classes of survival distributions; 3.5.3. Discrete-time models; 3.5.4. Continuous-time models; Chapter 4. Renewal Models; 4.1. Fundamental concepts and examples; 4.2. Waiting times; 4.3. Modified renewal processes; 4.4. Replacement models; 4.5. Renewal reward processes; 4.6. The risk problem of an insurance company; 4.7. Counter models; 4.7.1. Type I counters; 4.7.2. Type II counters; 4.8. Alternating renewal processes; 4.9. Superposition of renewal processes 327 $a4.10. Regenerative processesChapter 5. Semi-Markov Models; 5.1. Introduction; 5.2. Markov renewal processes; 5.2.1. Definitions; 5.2.2. Markov renewal theory; 5.3. First-passage times and state classification; 5.3.1. Stationary distribution and asymptotic results; 5.4. Reliability; 5.5. Reservoir models; 5.5.1. Model I; 5.5.2. Model II; 5.6. Queues; 5.6.1. The G/M/1 queue; 5.6.2. The M/G/1 queue; 5.7. Digital communication channels; Chapter 6. Branching Models; 6.1. The Bienayme?-Galton-Watson model; 6.1.1. Historical considerations; 6.1.2. Some elementary results; 6.1.3. A fundamental example 327 $a6.1.4. Extinction probability: critical theorem 330 $aThis book provides a pedagogical examination of the way in which stochastic models are encountered in applied sciences and techniques such as physics, engineering, biology and genetics, economics and social sciences. It covers Markov and semi-Markov models, as well as their particular cases: Poisson, renewal processes, branching processes, Ehrenfest models, genetic models, optimal stopping, reliability, reservoir theory, storage models, and queuing systems. Given this comprehensive treatment of the subject, students and researchers in applied sciences, as well as anyone looking for an introduc 410 0$aApplied stochastic methods series. 606 $aStochastic processes 606 $aStochastic models 615 0$aStochastic processes. 615 0$aStochastic models. 676 $a519.2/3 700 $aIosifescu$b Marius$041742 701 $aLimnios$b N$g(Nikolaos)$0900137 701 $aOprisan$b Gheorghe$0918059 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139240403321 996 $aIntroduction to stochastic models$92058478 997 $aUNINA LEADER 02244nam 2200409z- 450 001 9910688411903321 005 20210212 035 $a(CKB)4920000000094300 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/60921 035 $a(oapen)doab60921 035 $a(EXLCZ)994920000000094300 100 $a20202102d2018 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aTick-Host-Pathogen Interactions 210 $cFrontiers Media SA$d2018 215 $a1 online resource (543 p.) 225 1 $aFrontiers Research Topics 311 08$a2-88945-542-4 330 $aBesides causing direct damage associated with blood feeding and in some cases through the excretion of toxins with their saliva, the main relevance of ticks lies in the wide variety of pathogens that they can transmit, including viruses, bacteria, protozoa and helminths. Owing to socioeconomic and environmental changes, tick distribution is changing with incursions of ticks and tick-borne diseases occurring in different regions of the world when the widespread deployment of chemical acaricides and repellents has led to the selection of resistance in multiple populations of ticks. New approaches that are environmentally sustainable and that provide broad protection against current and future tick-borne pathogen (TBP) are thus urgently needed. Such development, however, requires improved understanding of factors resulting in vector competence and tick-host-pathogen interactions. 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