000 08254cam a2200949 i 4500
001 sulb-eb0032073
003 BD-SySUS
005 20170713221320.0
006 m o d
007 cr |||||||||||
008 121018s2013 enk ob 001 0 eng
010 _a 2012042679
040 _aDLC
_beng
_erda
_epn
_cDLC
_dYDX
_dDG1
_dN$T
_dYDXCP
_dE7B
_dUBY
_dCOO
_dNOC
_dUMI
_dDEBSZ
_dTEFOD
_dOCLCF
_dRECBK
_dEBLCP
_dMHW
_dTEFOD
_dOCLCQ
_dVT2
_dCDS
_dBD-SySUS
019 _a827207583
_a859157030
_a864913652
_a880597009
_a966465300
020 _a9781118477748
_q(ePub)
020 _a111847774X
_q(ePub)
020 _a9781118477809
_q(Adobe PDF)
020 _a1118477804
_q(Adobe PDF)
020 _a9781118477816
_q(MobiPocke)
020 _a1118477812
_q(MobiPocke)
020 _a9781118477793
_q(electronic bk.)
020 _a1118477790
_q(electronic bk.)
020 _z9781119944874
_q(hardback)
020 _z9781299188280
020 _z1299188281
020 _z1119944872
_q(hardback)
028 0 1 _aEB00063713
_bRecorded Books
029 1 _aAU@
_b000050562216
029 1 _aAU@
_b000052162463
029 1 _aCHNEW
_b000600026
029 1 _aDEBBG
_bBV041432883
029 1 _aDEBSZ
_b39747590X
029 1 _aDEBSZ
_b398288275
029 1 _aDEBSZ
_b43133241X
029 1 _aNZ1
_b15351000
029 1 _aNZ1
_b16175229
029 1 _aDEBSZ
_b452512662
029 1 _aDEBBG
_bBV043395134
035 _a(OCoLC)813568123
_z(OCoLC)827207583
_z(OCoLC)859157030
_z(OCoLC)864913652
_z(OCoLC)880597009
_z(OCoLC)966465300
037 _aCL0500000305
_bSafari Books Online
037 _aADDC87CE-FB5A-45D6-8AFC-C921E43C8F86
_bOverDrive, Inc.
_nhttp://www.overdrive.com
042 _apcc
050 0 0 _aQA274.7
072 7 _aMAT
_x029040
_2bisacsh
082 0 0 _a519.2/33
_223
084 _aMAT029000
_2bisacsh
049 _aMAIN
100 1 _aModica, Giuseppe.
245 1 2 _aA first course in probability and Markov chains /
_cGiuseppe Modica and Laura Poggiolini.
264 1 _aChichester :
_bWiley,
_c2013.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _a"Provides an introduction to basic structures of probability with a view towards applications in information technology A First Course in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main areas. The first part explores notions and structures in probability, including combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as weak and strong laws of large numbers and central limit theorem. In the second part of the book, focus is given to Discrete Time Discrete Markov Chains which is addressed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains. This book also looks at making use of measure theory notations that unify all the presentation, in particular avoiding the separate treatment of continuous and discrete distributions. A First Course in Probability and Markov Chains: Presents the basic elements of probability. Explores elementary probability with combinatorics, uniform probability, the inclusion-exclusion principle, independence and convergence of random variables. Features applications of Law of Large Numbers. Introduces Bernoulli and Poisson processes as well as discrete and continuous time Markov Chains with discrete states. Includes illustrations and examples throughout, along with solutions to problems featured in this book. The authors present a unified and comprehensive overview of probability and Markov Chains aimed at educating engineers working with probability and statistics as well as advanced undergraduate students in sciences and engineering with a basic background in mathematical analysis and linear algebra"--
_cProvided by publisher.
520 _a"A first course in Probability and Markov Chains presents an introduction to the basic elements in statistics and focuses in two main areas"--
_cProvided by publisher.
504 _aIncludes bibliographical references and index.
588 0 _aPrint version record and CIP data provided by publisher.
505 0 _aChapter 1 Combinatorics; 1.1 Binomial coefficients; 1.1.1 Pascal triangle; 1.1.2 Some properties of binomial coefficients; 1.1.3 Generalized binomial coefficients and binomial series; 1.1.4 Inversion formulas; 1.1.5 Exercises; 1.2 Sets, permutations and functions; 1.2.1 Sets; 1.2.2 Permutations; 1.2.3 Multisets; 1.2.4 Lists and functions; 1.2.5 Injective functions; 1.2.6 Monotone increasing functions; 1.2.7 Monotone nondecreasing functions; 1.2.8 Surjective functions; 1.2.9 Exercises; 1.3 Drawings; 1.3.1 Ordered drawings.
505 8 _a1.3.2 Simple drawings1.3.3 Multiplicative property of drawings; 1.3.4 Exercises; 1.4 Grouping; 1.4.1 Collocations of pairwise different objects; 1.4.2 Collocations of identical objects; 1.4.3 Multiplicative property; 1.4.4 Collocations in statistical physics; 1.4.5 Exercises; Chapter 2 Probability measures; 2.1 Elementary probability; 2.1.1 Exercises; 2.2 Basic facts; 2.2.1 Events; 2.2.2 Probability measures; 2.2.3 Continuity of measures; 2.2.4 Integral with respect to a measure; 2.2.5 Probabilities on finite and denumerable sets; 2.2.6 Probabilities on denumerable sets.
505 8 _a2.2.7 Probabilities on uncountable sets2.2.8 Exercises; 2.3 Conditional probability; 2.3.1 Definition; 2.3.2 Bayes formula; 2.3.3 Exercises; 2.4 Inclusion-exclusion principle; 2.4.1 Exercises; Chapter 3 Random variables; 3.1 Random variables; 3.1.1 Definitions; 3.1.2 Expected value; 3.1.3 Functions of random variables; 3.1.4 Cavalieri formula; 3.1.5 Variance; 3.1.6 Markov and Chebyshev inequalities; 3.1.7 Variational characterization of the median and of the expected value; 3.1.8 Exercises; 3.2 A few discrete distributions; 3.2.1 Bernoulli distribution; 3.2.2 Binomial distribution.
505 8 _a3.2.3 Hypergeometric distribution3.2.4 Negative binomial distribution; 3.2.5 Poisson distribution; 3.2.6 Geometric distribution; 3.2.7 Exercises; 3.3 Some absolutely continuous distributions; 3.3.1 Uniform distribution; 3.3.2 Normal distribution; 3.3.3 Exponential distribution; 3.3.4 Gamma distributions; 3.3.5 Failure rate; 3.3.6 Exercises; Chapter 4 Vector valued random variables; 4.1 Joint distribution; 4.1.1 Joint and marginal distributions; 4.1.2 Exercises; 4.2 Covariance; 4.2.1 Random variables with finite expected value and variance; 4.2.2 Correlation coefficient; 4.2.3 Exercises.
505 8 _a4.3 Independent random variables4.3.1 Independent events; 4.3.2 Independent random variables; 4.3.3 Independence of many random variables; 4.3.4 Sum of independent random variables; 4.3.5 Exercises; 4.4 Sequences of independent random variables; 4.4.1 Weak law of large numbers; 4.4.2 Borel-Cantelli lemma; 4.4.3 Convergences of random variables; 4.4.4 Strong law of large numbers; 4.4.5 A few applications of the law of large numbers; 4.4.6 Central limit theorem; 4.4.7 Exercises; Chapter 5 Discrete time Markov chains; 5.1 Stochastic matrices; 5.1.1 Definitions; 5.1.2 Oriented graphs.
650 0 _aMarkov processes.
650 4 _aMarkov processes.
650 7 _aMATHEMATICS
_xProbability & Statistics
_xGeneral.
_2bisacsh
650 7 _aMarkov processes.
_2fast
_0(OCoLC)fst01010347
650 7 _aMarkov processes.
_2local
655 4 _aElectronic books.
655 0 _aElectronic books.
700 1 _aPoggiolini, Laura.
776 0 8 _iPrint version:
_aModica, Giuseppe.
_tFirst course in probability and Markov chains
_z9781119944874
_w(DLC) 2012033463
856 4 0 _uhttp://onlinelibrary.wiley.com/book/10.1002/9781118477793
_zWiley Online Library [Free Download only for SUST IP]
938 _aEBL - Ebook Library
_bEBLB
_nEBL1120714
938 _aebrary
_bEBRY
_nebr10657903
938 _aEBSCOhost
_bEBSC
_n531372
938 _aRecorded Books, LLC
_bRECE
_nrbeEB00063713
938 _aYBP Library Services
_bYANK
_n10001571
938 _aYBP Library Services
_bYANK
_n10195827
938 _aYBP Library Services
_bYANK
_n9984561
994 _a92
_bDG1
999 _c63725
_d63725