Markov Chains: Gibbs Fields, Monte Carlo Simulation, and QueuesThis book discusses both the theory and applications of Markov chains. The author studies both discrete-time and continuous-time chains and connected topics such as finite Gibbs fields, non-homogeneous Markov chains, discrete time regenerative processes, Monte Carlo simulation, simulated annealing, and queueing networks are also developed in this accessible and self-contained text. The text is firstly an introduction to the theory of stochastic processes at the undergraduate or beginning graduate level. Its primary objective is to initiate the student to the art of stochastic modelling. The treatment is mathematical, with definitions, theorems, proofs and a number of classroom examples which help the student to fully grasp the content of the main results. Problems of varying difficulty are proposed at the close of each chapter. The text is motivated by significant applications and progressively brings the student to the borders of contemporary research. Students and researchers in operations research and electrical engineering as well as in physics, biology and the social sciences will find this book of interest. |
Contents
III | 1 |
IV | 3 |
V | 4 |
VI | 7 |
VII | 9 |
VIII | 11 |
IX | 13 |
X | 15 |
LXXXIX | 212 |
XC | 215 |
XCI | 219 |
XCII | 223 |
XCIII | 226 |
XCIV | 230 |
XCV | 232 |
XCVI | 235 |
XI | 20 |
XII | 24 |
XIII | 27 |
XIV | 29 |
XVII | 33 |
XVIII | 36 |
XIX | 37 |
XX | 39 |
XXII | 41 |
XXIII | 42 |
XXIV | 43 |
XXV | 45 |
XXVI | 47 |
XXVII | 53 |
XXX | 56 |
XXXI | 58 |
XXXII | 59 |
XXXIII | 65 |
XXXIV | 68 |
XXXV | 71 |
XXXVI | 72 |
XXXVII | 75 |
XXXVIII | 76 |
XXXIX | 80 |
XL | 81 |
XLI | 83 |
XLII | 86 |
XLIII | 95 |
XLVI | 97 |
XLVII | 100 |
XLIX | 104 |
L | 105 |
LI | 110 |
LII | 113 |
LIII | 117 |
LIV | 125 |
LVII | 128 |
LVIII | 130 |
LIX | 131 |
LX | 133 |
LXI | 136 |
LXII | 137 |
LXIII | 140 |
LXIV | 142 |
LXV | 145 |
LXVI | 146 |
LXVII | 149 |
LXVIII | 153 |
LXIX | 154 |
LXXI | 156 |
LXXII | 167 |
LXXIII | 173 |
LXXIV | 178 |
LXXVI | 180 |
LXXVII | 185 |
LXXVIII | 186 |
LXXIX | 189 |
LXXX | 195 |
LXXXIV | 199 |
LXXXV | 201 |
LXXXVI | 204 |
LXXXVII | 207 |
LXXXVIII | 211 |
XCVIII | 238 |
XCIX | 239 |
C | 241 |
CI | 242 |
CII | 253 |
CIV | 256 |
CV | 260 |
CVI | 261 |
CVII | 268 |
CVIII | 270 |
CIX | 275 |
CX | 279 |
CXI | 280 |
CXII | 281 |
CXIII | 285 |
CXIV | 288 |
CXV | 290 |
CXVII | 295 |
CXVIII | 299 |
CXIX | 305 |
CXXI | 311 |
CXXII | 323 |
CXXV | 324 |
CXXVI | 327 |
CXXVII | 329 |
CXXVIII | 333 |
CXXIX | 338 |
CXXX | 340 |
CXXXI | 344 |
CXXXII | 345 |
CXXXIV | 348 |
CXXXV | 350 |
CXXXVI | 357 |
CXXXVII | 361 |
CXXXVIII | 363 |
CXL | 364 |
CXLI | 369 |
CXLV | 372 |
CXLVI | 375 |
CXLVII | 378 |
CXLVIII | 380 |
CXLIX | 383 |
CL | 385 |
CLI | 388 |
CLII | 394 |
CLIII | 398 |
CLIV | 402 |
CLV | 407 |
CLVI | 417 |
CLVII | 418 |
CLVIII | 420 |
CLIX | 422 |
CLX | 423 |
CLXII | 424 |
CLXIII | 426 |
CLXIV | 427 |
CLXV | 428 |
CLXVI | 430 |
| 433 | |
| 439 | |
| 441 | |
Other editions - View all
Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues Pierre Bremaud No preview available - 2013 |
Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues Pierre Bremaud No preview available - 2010 |
Common terms and phrases
algorithm average number birth-and-death bounded called Chapter compute configuration continuous-time HMC corresponding defined Definition denoted discrete-time eigenvalue equality event Example exists exponential finite state space follows formula function Gibbs sampler given gives independent inequality infinite infinitesimal initial distribution integer invariant measure irreducible Lemma Let X}nzo Markov chain Markov property martingale nonnegative notation null observe obtain P(X₁ particular point process Poisson process Poisson system positive recurrent probability distribution Problem Proof prove queue random field random variables random vector random walk regular jump HMC renewal equation resp respect result reversible S₁ satisfied semigroup Show simulated annealing simulation solution stationary distribution stochastic matrix stochastic process strong Markov property sufficient condition Suppose t₁ Theorem 2.1 theory transient transition matrix transition semigroup u₁ v₁ values weak ergodicity X₁ Xn+1 Zn+1
Popular passages
Page 434 - An interruptible algorithm for perfect sampling via Markov chains. Annals of Applied Probability, 8(1):131-162, 1998.