LEADER 03765nam  22006135  450 
001 9910300157403321
005 20200630072242.0
010   $a3-642-40523-1
024 7 $a10.1007/978-3-642-40523-5
035   $a(CKB)3710000000078652
035   $a(MH)013863957-4
035   $a(SSID)ssj0001049494
035   $a(PQKBManifestationID)11602091
035   $a(PQKBTitleCode)TC0001049494
035   $a(PQKBWorkID)11020042
035   $a(PQKB)10276716
035   $a(DE-He213)978-3-642-40523-5
035   $a(MiAaPQ)EBC3107081
035   $a(PPN)17611484X
035   $a(EXLCZ)993710000000078652
100   $a20131028d2014      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cn$2rdamedia
183   $anc$2rdacarrier
200 13$aAn Introduction to Markov Processes /$fby Daniel W. Stroock
205   $a2nd ed. 2014.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014.
215   $a1 online resource (xv, 203 pages )
225 1 $aGraduate Texts in Mathematics,$x0072-5285 ;$v230
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-642-40522-3 
320   $aIncludes bibliographical references (page 199) and index.
327   $aPreface -- Random Walks, a Good Place to Begin -- Doeblin's Theory for Markov Chains -- Stationary Probabilities -- More about the Ergodic Theory of Markov Chains -- Markov Processes in Continuous Time -- Reversible Markov Processes -- A minimal Introduction to Measure Theory -- Notation -- References -- Index.
330   $aThis book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology. Topics covered are: Doeblin's theory, general ergodic properties, and continuous time processes. Applications are dispersed throughout the book. In addition, a whole chapter is devoted to reversible processes and the use of their associated Dirichlet forms to estimate the rate of convergence to equilibrium. These results are then applied to the analysis of the Metropolis (a.k.a simulated annealing) algorithm. The corrected and enlarged 2nd edition contains a new chapter in which the author develops computational methods for Markov chains on a finite state space. Most intriguing is the section with a new technique for computing stationary measures, which is applied to derivations of Wilson's algorithm and Kirchoff's formula for spanning trees in a connected graph.
410  0$aGraduate Texts in Mathematics,$x0072-5285 ;$v230
606   $aProbabilities
606   $aDynamics
606   $aErgodic theory
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aDynamical Systems and Ergodic Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M1204X
615  0$aProbabilities.
615  0$aDynamics.
615  0$aErgodic theory.
615 14$aProbability Theory and Stochastic Processes.
615 24$aDynamical Systems and Ergodic Theory.
676   $a519.2
700   $aStroock$b Daniel W$4aut$4http://id.loc.gov/vocabulary/relators/aut$042628
906   $aBOOK
912   $a9910300157403321
996   $aIntroduction to Markov processes$933145
997   $aUNINA
999   $aThis Record contains information from the Harvard Library Bibliographic Dataset, which is provided by the Harvard Library under its Bibliographic Dataset Use Terms and includes data made available by, among others the Library of Congress