LEADER 05526nam 2200733Ia 450 001 9911019641403321 005 20200520144314.0 010 $a9786610271832 010 $a9780470864289 010 $a0470864281 010 $a9781280271830 010 $a1280271833 010 $a9780470358337 010 $a0470358335 010 $a9780470013632 010 $a047001363X 035 $a(CKB)1000000000019117 035 $a(StDuBDS)AH13353597 035 $a(SSID)ssj0000296464 035 $a(PQKBManifestationID)11253823 035 $a(PQKBTitleCode)TC0000296464 035 $a(PQKBWorkID)10321749 035 $a(PQKB)10665671 035 $a(SSID)ssj0000155476 035 $a(PQKBManifestationID)12054104 035 $a(PQKBTitleCode)TC0000155476 035 $a(PQKBWorkID)10112233 035 $a(PQKB)10819822 035 $a(MiAaPQ)EBC158138 035 $a(PPN)137734271 035 $a(Perlego)2748696 035 $a(EXLCZ)991000000000019117 100 $a20021118d2003 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 12$aA first course in stochastic models /$fHenk C. Tijms 205 $a2nd ed. 210 $aChichester, England ;$aHoboken, NJ $cWiley$dc2003 215 $a1 online resource (450 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9780471498810 311 08$a0471498815 311 08$a9780471498803 311 08$a0471498807 320 $aIncludes bibliographical references and index. 327 $aPreface. The Poisson Process and Related Processes. Renewal-Reward Processes. Discrete-Time Markov Chains. Continuous-Time Markov Chains. Markov Chains and Queues. Discrete-Time Markov Decision Processes. Semi-Markov Decision Processes. Advanced Renewal Theory. Algorithmic Analysis of Queueing Models. Appendices. Appendix A: Useful Tools in Applied Probability. Appendix B: Useful Probability Distributions. Appendix C: Generating Functions. Appendix D: The Discrete Fast Fourier Transform. Appendix E: Laplace Transform Theory. Appendix F: Numerical Laplace Inversion. Appendix G: The Root-Finding Problem. References. Index. 330 $aAn integrated presentation of theory, applications and algorithms that demonstrates how useful simple stochastic models can be for gaining insight into the behaviour of complex stochastic systems. 330 $bThe field of applied probability has changed profoundly in the past twenty years. The development of computational methods has greatly contributed to a better understanding of the theory. A First Course in Stochastic Models provides a self-contained introduction to the theory and applications of stochastic models. Emphasis is placed on establishing the theoretical foundations of the subject, thereby providing a framework in which the applications can be understood. Without this solid basis in theory no applications can be solved. Provides an introduction to the use of stochastic models through an integrated presentation of theory, algorithms and applications. Incorporates recent developments in computational probability. Includes a wide range of examples that illustrate the models and make the methods of solution clear. Features an abundance of motivating exercises that help the student learn how to apply the theory. Accessible to anyone with a basic knowledge of probability. A First Course in Stochastic Models is suitable for senior undergraduate and graduate students from computer science, engineering, statistics, operations resear ch, and any other discipline where stochastic modelling takes place. It stands out amongst other textbooks on the subject because of its integrated presentation of theory, algorithms and applications. The field of applied probability has changed profoundly in the past twenty years. The development of computational methods has greatly contributed to a better understanding of the theory. A First Course in Stochastic Models provides a self-contained introduction to the theory and applications of stochastic models. Emphasis is placed on establishing the theoretical foundations of the subject, thereby providing a framework in which the applications can be understood. Without this solid basis in theory no applications can be solved. Provides an introduction to the use of stochastic models through an integrated presentation of theory, algorithms and applications. Incorporates recent developments in computational probability. Includes a wide range of examples that illustrate the models and make the methods of solution clear. Features an abundance of motivating exercises that help the student learn how to apply the theory. Accessible to anyone with a basic knowledge of probability. A First Course in Stochastic Models is suitable for senior undergraduate and graduate students from computer science, engineering, statistics, operations resear ch, and any other discipline where stochastic modelling takes place. It stands out amongst other textbooks on the subject because of its integrated presentation of theory, algorithms and applications. 606 $aStochastic processes 606 $aProbabilities 615 0$aStochastic processes. 615 0$aProbabilities. 676 $a519.2/3 700 $aTijms$b H. C$056125 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019641403321 996 $aFirst course in stochastic models$9736670 997 $aUNINA