Probability and Random Processes: Edition 4

¡
¡ Oxford University Press
āχāĻŦ⧁āĻ•
688
āĻĒ⧃āĻˇā§āĻ āĻž
āϝ⧋āĻ—ā§āϝ
āĻŽā§‚āĻ˛ā§āϝāĻžāĻ‚āĻ•āύ āφ⧰⧁ āĻĒā§°ā§āϝāĻžāϞ⧋āϚāύāĻž āϏāĻ¤ā§āϝāĻžāĻĒāύ āϕ⧰āĻž āĻšā§‹ā§ąāĻž āύāĻžāχ  āĻ…āϧāĻŋāĻ• āϜāĻžāύāĻ•

āĻāχ āχāĻŦ⧁āĻ•āĻ–āύ⧰ āĻŦāĻŋāĻˇā§Ÿā§‡

The fourth edition of this successful text provides an introduction to probability and random processes, with many practical applications. It is aimed at mathematics undergraduates and postgraduates, and has four main aims. US BL To provide a thorough but straightforward account of basic probability theory, giving the reader a natural feel for the subject unburdened by oppressive technicalities. BE BL To discuss important random processes in depth with many examples.BE BL To cover a range of topics that are significant and interesting but less routine.BE BL To impart to the beginner some flavour of advanced work.BE UE OP The book begins with the basic ideas common to most undergraduate courses in mathematics, statistics, and science. It ends with material usually found at graduate level, for example, Markov processes, (including Markov chain Monte Carlo), martingales, queues, diffusions, (including stochastic calculus with Itô's formula), renewals, stationary processes (including the ergodic theorem), and option pricing in mathematical finance using the Black-Scholes formula. Further, in this new revised fourth edition, there are sections on coupling from the past, LÊvy processes, self-similarity and stability, time changes, and the holding-time/jump-chain construction of continuous-time Markov chains. Finally, the number of exercises and problems has been increased by around 300 to a total of about 1300, and many of the existing exercises have been refreshed by additional parts. The solutions to these exercises and problems can be found in the companion volume, One Thousand Exercises in Probability, third edition, (OUP 2020).CP

āϞāĻŋāĻ–āϕ⧰ āĻŦāĻŋāώāϝāĻŧ⧇

Geoffrey Grimmett is Professor Emeritus of Mathematical Statistics at the University of Cambridge. Cambridge has been his base for pursuing probability theory and the mathematics of disordered systems since 1992. He was Master of Downing College, Cambridge from 2013-2018 and has been appointed Chair of the Heilbronn Institute for Mathematical Research from 2020. He has written numerous research articles in probability theory and statistical mechanics, as well as three research books. With David Stirzaker and Dominic Welsh respectively, he has co-authored two successful textbooks on probability and random processes at the undergraduate and postgraduate levels. David Stirzaker was educated at Oxford University and Berkeley before being appointed as Fellow and Tutor in Applied Mathematics at St John's College, Oxford. He is now an Emeritus Research Fellow at St John's College, and an Emeritus Professor at the Mathematical Institute, Oxford. He has written five textbooks on probability and random processes, two of them jointly with Geoffrey Grimmett. Most recently, (2015), he has written The Cambridge Dictionary of Probability and its Applications.

āĻāχ āχāĻŦ⧁āĻ•āĻ–āύāĻ• āĻŽā§‚āĻ˛ā§āϝāĻžāĻ‚āĻ•āύ āϕ⧰āĻ•

āφāĻŽāĻžāĻ• āφāĻĒā§‹āύāĻžā§° āĻŽāϤāĻžāĻŽāϤ āϜāύāĻžāĻ“āĻ•āĨ¤

āĻĒāĻĸāĻŧāĻžā§° āύāĻŋāĻ°ā§āĻĻ⧇āĻļāĻžā§ąāϞ⧀

āĻ¸ā§āĻŽāĻžā§°ā§āϟāĻĢ’āύ āφ⧰⧁ āĻŸā§‡āĻŦāϞ⧇āϟ
Android āφ⧰⧁ iPad/iPhoneā§° āĻŦāĻžāĻŦ⧇ Google Play Books āĻāĻĒāĻŸā§‹ āχāύāĻˇā§āϟāϞ āϕ⧰āĻ•āĨ¤ āχ āĻ¸ā§āĻŦāϝāĻŧāĻ‚āĻ•ā§āϰāĻŋāϝāĻŧāĻ­āĻžā§ąā§‡ āφāĻĒā§‹āύāĻžā§° āĻāĻ•āĻžāωāĻŖā§āϟ⧰ āϏ⧈āϤ⧇ āĻ›āĻŋāĻ‚āĻ• āĻšāϝāĻŧ āφ⧰⧁ āφāĻĒ⧁āύāĻŋ āϝ'āϤ⧇ āύāĻžāĻĨāĻžāĻ•āĻ• āϤ'āϤ⧇āχ āϕ⧋āύ⧋ āĻ…āĻĄāĻŋāĻ…'āĻŦ⧁āĻ• āĻ…āύāϞāĻžāχāύ āĻŦāĻž āĻ…āĻĢāϞāĻžāχāύāϤ āĻļ⧁āύāĻŋāĻŦāϞ⧈ āϏ⧁āĻŦāĻŋāϧāĻž āĻĻāĻŋāϝāĻŧ⧇āĨ¤
āϞ⧇āĻĒāϟāĻĒ āφ⧰⧁ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžā§°
āφāĻĒ⧁āύāĻŋ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžā§°ā§° ā§ąā§‡āĻŦ āĻŦā§āϰāĻžāωāϜāĻžā§° āĻŦā§āĻ¯ā§ąāĻšāĻžā§° āϕ⧰āĻŋ Google PlayāϤ āĻ•āĻŋāύāĻž āĻ…āĻĄāĻŋāĻ…'āĻŦ⧁āĻ•āϏāĻŽā§‚āĻš āĻļ⧁āύāĻŋāĻŦ āĻĒāĻžā§°ā§‡āĨ¤
āχ-ā§°ā§€āĻĄāĻžā§° āφ⧰⧁ āĻ…āĻ¨ā§āϝ āĻĄāĻŋāĻ­āĻžāχāϚ
Kobo eReadersā§° āĻĻ⧰⧇ āχ-āϚāĻŋ⧟āĻžāρāĻšā§€ā§° āĻĄāĻŋāĻ­āĻžāχāϚāϏāĻŽā§‚āĻšāϤ āĻĒā§āĻŋāĻŦāϞ⧈, āφāĻĒ⧁āύāĻŋ āĻāϟāĻž āĻĢāĻžāχāϞ āĻĄāĻžāωāύāĻ˛â€™āĻĄ āϕ⧰āĻŋ āϏ⧇āχāĻŸā§‹ āφāĻĒā§‹āύāĻžā§° āĻĄāĻŋāĻ­āĻžāχāϚāϞ⧈ āĻ¸ā§āĻĨāĻžāύāĻžāĻ¨ā§āϤ⧰āĻŖ āϕ⧰āĻŋāĻŦ āϞāĻžāĻ—āĻŋāĻŦāĨ¤ āϏāĻŽā§°ā§āĻĨāĻŋāϤ āχ-ā§°āĻŋāĻĄāĻžā§°āϞ⧈ āĻĢāĻžāχāϞāĻŸā§‹ āϕ⧇āύ⧇āĻ•ā§ˆ āĻ¸ā§āĻĨāĻžāύāĻžāĻ¨ā§āϤ⧰ āϕ⧰āĻŋāĻŦ āϜāĻžāύāĻŋāĻŦāϞ⧈ āϏāĻšāĻžāϝāĻŧ āϕ⧇āĻ¨ā§āĻĻā§ā§°āϤ āĻĨāĻ•āĻž āϏāĻŦāĻŋāĻļ⧇āώ āύāĻŋā§°ā§āĻĻ⧇āĻļāĻžā§ąāϞ⧀ āϚāĻžāĻ“āĻ•āĨ¤