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E-grāmata: Large Deviations and Idempotent Probability

  • Formāts: 520 pages
  • Izdošanas datums: 07-May-2001
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781040201640
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  • Formāts: 520 pages
  • Izdošanas datums: 07-May-2001
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781040201640

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Noted researcher in the modern theory of large deviations, Puhalskii (mathematics, U. of Colorado-Denver) expounds on the recent methodology of building large deviation theory along the lines of weak convergence theory. He develops an idempotent, or maxitive, probability theory, and introduces maxingales as idempotent analogues of martingales, Wiener and Poisson processes, and Ito differential equations and studies their properties. He also formulates what he calls a large deviation convergence, a large deviation principle for stochastic processes. Annotation c. Book News, Inc., Portland, OR (booknews.com)

In the view of many probabilists, author Anatolii Puhalskii's research results stand among the most significant achievements in the modern theory of large deviations. In fact, his work marked a turning point in the depth of our understanding of the connections between the large deviation principle (LDP) and well-known methods for establishing weak convergence results.

Large Deviations and Idempotent Probability expounds upon the recent methodology of building large deviation theory along the lines of weak convergence theory. The author develops an idempotent (or maxitive) probability theory, introduces idempotent analogues of martingales (maxingales), Wiener and Poisson processes, and Ito differential equations, and studies their properties. The large deviation principle for stochastic processes is formulated as a certain type of convergence of stochastic processes to idempotent processes. The author calls this large deviation convergence.

The approach to establishing large deviation convergence uses novel compactness arguments. Coupled with the power of stochastic calculus, this leads to very general results on large deviation asymptotics of semimartingales. Large and moderate deviation asymptotics are treated in a unified manner.

Starting with the foundations of idempotent measure theory and culminating in applications to large deviation asymptotics of queueing systems, Large Deviations and Idempotent Probability offers an outstanding opportunity to examine both the development of a remarkable approach and recently discovered results as presented by one of the foremost leaders in the field.
Preface xi
Basic notation 1(2)
I Indempotent Probability Theory 3(248)
Idempotent Probability measures
5(86)
Idempotent measures
5(9)
Measurable functions
14(3)
Models of Convergence
17(3)
Idempotent integration
20(12)
Product spaces
32(4)
Independence and conditioning
36(15)
Idempotent measures on topological spaces
51(7)
Idempotent measures on projective limits
58(6)
Topological spaces of idempotent probabilities
64(15)
Derived weak convergence
79(5)
Laplace-Fenchel transform
84(7)
Maxingales
91(160)
Idempotent stopping times
91(4)
Idempotent processes
95(9)
Exponential maxingales
104(10)
Wiener and Poisson idempotent processes
114(10)
Idempotent stochastic integrals
124(27)
Idempotent Ito differential equations
151(19)
Semimaxingales
170(32)
Maxingale problems
202(49)
II Large Deviation Convergence of Semimartingales 251(206)
Large deviation convergence
253(36)
Large deviation convergence in Tihonov spaces
253(23)
Large deviation convergence in the Skorohod space
276(13)
The method of finite-dimensional distributions
289(66)
Convergence of stochastic exponentials
290(15)
Convergence of characteristics
305(27)
The case of small jumps
316(9)
The general case
325(7)
Corollaries
332(10)
Applications to partial-sum processes
342(13)
The method of the maxingale problem
355(78)
Convergence of stochastic exponentials
356(17)
Proofs
360(13)
Convergence of characteristics
373(33)
Exponential tightness results
380(11)
LD accumulation points as solutions to maxingale problems
391(13)
Proofs of the Main results
404(2)
Large deviation convergence results
406(8)
Large deviation convergence of Markov processes
414(19)
Large deviation convergence of queueing processes
433(24)
Moderate deviations in queueing networks
433(17)
Idempotent diffusion approximation for single server queues
433(9)
Idempotent diffusion approximation for queueing networks
442(8)
Very large and moderate deviations for many server queues
450(7)
Appendix A Auxiliary lemmas 457(10)
Appendix B Notes and remarks 467(16)
Bibliography 483(12)
Index 495
Puhalskii, Anatolii