A first course in probability and Markov chains /

"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 an...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Modica, Giuseppe
Άλλοι συγγραφείς: Poggiolini, Laura
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Chichester : Wiley, 2013.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Chapter 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.
  • 1.3.2 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.
  • 2.2.7 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.
  • 3.2.3 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.
  • 4.3 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.