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|a DLC
|c DLC
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|d OrLoB-B
|d OCoLC
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| 050 |
0 |
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|a QA274.7
|b .N67 1997
|
| 082 |
0 |
0 |
|a 519.2/33
|2 20
|
| 100 |
1 |
|
|a Norris, J. R.
|q (James R.)
|0 http://id.loc.gov/authorities/names/n88013908
|1 http://viaf.org/viaf/34532163
|
| 245 |
1 |
0 |
|a Markov chains /
|c J.R. Norris.
|
| 260 |
|
|
|a Cambridge [England] ;
|a New York :
|b Cambridge University Press,
|c 1997.
|
| 300 |
|
|
|a xvi, 237 p. :
|b ill. ;
|c 26 cm.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|0 http://id.loc.gov/vocabulary/contentTypes/txt
|
| 337 |
|
|
|a unmediated
|b n
|2 rdamedia
|0 http://id.loc.gov/vocabulary/mediaTypes/n
|
| 338 |
|
|
|a volume
|b nc
|2 rdacarrier
|0 http://id.loc.gov/vocabulary/carriers/nc
|
| 440 |
|
0 |
|a Cambridge series on statistical and probabilistic mathematics
|
| 504 |
|
|
|a Includes bibliographical references (p. [232]-233) and index.
|
| 505 |
0 |
0 |
|g 1.
|t Discrete-time Markov chains.
|t 1.1 Definition and basic properties.
|t 1.2 Class structure.
|t 1.3 Hitting times and absorption probabilities.
|t 1.4 Strong Markov property.
|t 1.5 Recurrence and transience.
|t 1.6 Recurrence and transience of random walks.
|t 1.7 Invariant distributions.
|t 1.8 Convergence to equilibrium.
|t 1.9 Time reversal.
|t 1.10 Ergodic theorem.
|t 1.11 Appendix: recurrence relations.
|t 1.12 Appendix: asymptotics for n! --
|g 2.
|t Continuous-time Markov chains I.
|t 2.1 Q-matrices and their exponentials.
|t 2.2 Continuous-time random processes.
|t 2.3 Some properties of the exponential distribution.
|t 2.4 Poisson processes.
|t 2.5 Birth processes.
|t 2.6 Jump chain and holding times.
|t 2.7 Explosion.
|t 2.8 Forward and backward equations.
|t 2.9 Non-minimal chains.
|t 2.10 Appendix: matrix exponentials --
|g 3.
|t Continuous-time Markov chains II.
|t 3.1 Basic properties.
|t 3.2 Class structure.
|t 3.3 Hitting times and absorption probabilities.
|t 3.4 Recurrence and transience.
|t 3.5 Invariant distributions.
|t 3.6 Convergence to equilibrium.
|t 3.7 Time reversal.
|t 3.8 Ergodic theorem --
|g 4.
|t Further theory.
|t 4.1 Martingales.
|t 4.2 Potential theory.
|t 4.3 Electrical networks.
|t 4.4 Brownian motion --
|g 5.
|t Applications.
|t 5.1 Markov chains in biology.
|t 5.2 Queues and queueing networks.
|t 5.3 Markov chains in resource management.
|t 5.4 Markov decision processes.
|t 5.5 Markov chain Monte Carlo --
|g 6.
|t Appendix: probability and measure.
|t 6.1 Countable sets and countable sums.
|t 6.2 Basic facts of measure theory.
|t 6.3 Probability spaces and expectation.
|t 6.4 Monotone convergence and Fubini's theorem.
|t 6.5 Stopping times and the strong Markov property.
|t 6.6 Uniqueness of probabilities and independence of [omega]-algebras.
|
| 650 |
|
0 |
|a Markov processes.
|0 http://id.loc.gov/authorities/subjects/sh85081369
|
| 650 |
|
7 |
|a Markov processes.
|2 fast
|0 http://id.worldcat.org/fast/fst01010347
|
| 901 |
|
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|a ToCBNA
|
| 903 |
|
|
|a HeVa
|
| 035 |
|
|
|a (OCoLC)41179747
|
| 929 |
|
|
|a cat
|
| 999 |
f |
f |
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| 928 |
|
|
|t Library of Congress classification
|a QA274.7 .N67 1997
|l JCL
|c JCL-Sci
|i 3298651
|
| 927 |
|
|
|t Library of Congress classification
|a QA274.7 .N67 1997
|l JCL
|c JCL-Sci
|e CRERAR
|b 44785586
|i 4794233
|
