LEADER 03061nam 2200625 450 001 996466658403316 005 20220428102909.0 010 $a3-540-36098-0 024 7 $a10.1007/BFb0079113 035 $a(CKB)1000000000438708 035 $a(SSID)ssj0000325852 035 $a(PQKBManifestationID)12134863 035 $a(PQKBTitleCode)TC0000325852 035 $a(PQKBWorkID)10265072 035 $a(PQKB)11735403 035 $a(DE-He213)978-3-540-36098-8 035 $a(MiAaPQ)EBC5590939 035 $a(MiAaPQ)EBC6700101 035 $a(Au-PeEL)EBL5590939 035 $a(OCoLC)1066177909 035 $a(Au-PeEL)EBL6700101 035 $a(PPN)155193864 035 $a(EXLCZ)991000000000438708 100 $a20220428d1969 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aProbability and information theory $eproceedings of the international symposium at Mcmaster University, Canada, April 1968 /$fedited by M. Behara, K. Krickeberg, J. Wolfowitz 205 $a1st ed. 1969. 210 1$aBerlin ;$aHeidelberg :$cSpringer-Verlag,$d[1969] 210 4$dİ1969 215 $a1 online resource (IV, 260 p.) 225 1 $aLecture Notes in Mathematics ;$v89 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-387-04608-9 311 $a3-540-04608-9 327 $aOn different characterizations of entropies -- The structure of capacity functions for compound channels -- Boolean algebraic methods in Markov chains -- Maxima of partial sums -- Series expansions for random processes -- Glivenko-Cantelli type theorems for distance functions based on the modified empirical distribution function of M. Kac and for the empirical process with random sample size in general -- On the continuity of Markov processes -- Some mathematical problems in statistical mechanics -- Asymptotic behaviour of the average probability of error for low rates of information transmission -- On the optimum rate of transmitting information -- A necessary and sufficient condition for the validity of the local ergodic theorem -- Recent results on mixing in topological measure spaces -- Convergence in probability and allied results -- Applications of almost surely convergent constructions of weakly convergent processes -- Random processes defined through the interaction of an infinite particle system -- The central limit theorem and ?-entropy -- Maximum probability estimators with a general loss function. 410 0$aLecture notes in mathematics (Springer-Verlag) ;$v89. 606 $aProbabilities$vCongresses 606 $aInformation theory$vCongresses 615 0$aProbabilities 615 0$aInformation theory 676 $a519.2 702 $aBehara$b M. 702 $aKrickeberg$b Klaus 702 $aWolfowitz$b Jacob$f1910- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466658403316 996 $aProbability and Information Theory$983211 997 $aUNISA