Record ID | ia:gaussianapproxim00gave |
Source | Internet Archive |
Download MARC XML | https://archive.org/download/gaussianapproxim00gave/gaussianapproxim00gave_marc.xml |
Download MARC binary | https://www.archive.org/download/gaussianapproxim00gave/gaussianapproxim00gave_meta.mrc |
LEADER: 01949nam 2200421 a 4500
001 ocn428731684
003 OCoLC
005 20100622112311.5
008 090730s1975 caua b f000 0 eng d
035 $a
035 $a
037 $aADA013486
040 $aAD#$cAD#
049 $aAD#A
086 0 $aD 208.14/2:NPS-55GV75061
088 $aNPS-55GV75061
100 1 $aGaver, Donald Paul.
245 10 $aGaussian approximations to service problems :$ba communication system example /$cby D. P. Gaver and J. P. Lehoczky.
260 $aMonterey, California :$bNaval Postgraduate School,$c1975.
300 $a36 p. :$bill. ;$c28 cm.
500 $aTitle from cover.
500 $a"NPS55Gv75061"--Cover.
500 $a"June 1975"--Cover.
504 $aIncludes bibliographical references (p. 34)
506 $a"Approved for public release; distribution is unlimited"--Cover.
513 $aTechnical report; 1975.
520 $aMessages arrive at a group of service channels in accordance with a time-dependent Poisson process. An arrival either (1) immediately begins k-stage Markovian service if an empty channel is reached, or (2) balks and enters a retrial population if the channel sought is busy. Diffusion approximations to the number of messages in service (each stage) and in the retrial population are derived by writing stochastic differential (I+0) equations. Steady-state distributions are found and compared with certain simulation results.
650 0 $aStochastic differential equations.
650 0 $aQueuing theory.
650 0 $aCommunication$xMathematical models.
700 1 $aLehoczky, John P.
710 2 $aNaval Postgraduate School (U.S.)
994 $aC0$bAD#
035 $a
035 $a
949 $lgen$nL$aQA274.23$b.G2$s1$tnorm$u00001$i32768001724255$d missing
949 $lgen$nL$aQA274.23$b.G2$s1$tnorm$u00001$i32768001724263
926 $aNPS-LIB$bDIGIPROJ$cD 208.14/2:NPS-55GV75061$dTECH_RPT$eNEVER$f1