LEADER 03597nam0 22005653i 450 001 VAN00267821 005 20260629121631.822 017 70$2N$a9781461298526 035 40$a799161193 100 $a20231124d1974 |0itac50 ba 101 $aeng 102 $aUS 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aRandom Processes$fM. Rosenblatt 205 $a2. ed 210 $aNew York$cSpringer-Verlag$d1974 215 $ax, 228 p.$cill.$d24 cm 327 $aThis text has as its object an introduction to elements of the theory of random processes. Strictly speaking, only a good background in the topics usually associated with a course in Advanced Calculus (see, for example, the text of Apostol [1]) and the elements of matrix algebra is required although additional background is always helpful. N onethe­ less a strong effort has been made to keep the required background on the level specified above. This means that a course based on this book would be appropriate for a beginning graduate student or an advanced undergraduate. Previous knowledge of probability theory is not required since the discussion starts with the basic notions of probability theory. Chapters II and III are concerned with discrete probability spaces and elements of the theory of Markov chains respectively. These two chapters thus deal with probability theory for finite or countable models. The object is to present some of the basic ideas and problems of the theory in a discrete context where difficulties of heavy technique and detailed measure theoretic discussions do not obscure the ideas and problems. 410 1$1001VAN00023579$12001 $aGraduate texts in mathematics$1210 $aNew York [etc.]$cSpringer$d1950-$v17 606 $a28D05$xMeasure-preserving transformations [MSC 2020]$3VANC022530$2MF 606 $a60-XX$xProbability theory and stochastic processes [MSC 2020]$3VANC020428$2MF 606 $a60A05$xAxioms; other general questions in probability [MSC 2020]$3VANC021400$2MF 606 $a60E05$xProbability distributions: general theory [MSC 2020]$3VANC024596$2MF 606 $a60F05$xCentral limit and other weak theorems [MSC 2020]$3VANC024652$2MF 606 $a60Gxx$xStochastic processes [MSC 2020]$3VANC020000$2MF 606 $a60Jxx$xMarkov processes [MSC 2020]$3VANC019842$2MF 606 $a62M10$xTime series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020]$3VANC025079$2MF 606 $a62M15$xInference from stochastic processes and spectral analysis [MSC 2020]$3VANC027804$2MF 610 $aConditional probability$9KW:K 610 $aLinear optimization$9KW:K 610 $aPoisson distribution$9KW:K 610 $aProbability spaces$9KW:K 610 $aProbability theory$9KW:K 610 $aRandom Variables$9KW:K 610 $aStochastic processes$9KW:K 610 $aVariance$9KW:K 620 $aUS$dNew York$3VANL000011 700 1$aRosenblatt$bMurray$3VANV207135$012734 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20260828$gRICA 856 4 $uhttps://doi.org/10.1007/978-1-4612-9852-6$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN00267821 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08DLOAD e-book 7323 $e08eMF7323 20231127 996 $aRandom processes$945198 997 $aUNICAMPANIA