01084cam0-2200349---450-99000580694040332120160614154017.02-7227-0023-9000580694FED01000580694(Aleph)000580694FED0100058069419990604d1990----km-y0itay50------bafreFR--------001gy<<La >>chanson de Bertrand du Guesclinde Cuvelier[éd. par] Jean-Claude Fauconpréface de Philippe MenardToulouseEditions universitaires du Sud19903 v.24 cm841.122itaCuvelier<sec. 14>Faucon,Jean-ClaudeMénard,Philippe<1935- >ITUNINARICAUNIMARCBK990005806940403321841.1 CUV 1(1)Dip.f.m.4661/AFLFBC841.1 CUV 1(2)Dip.f.m.4661/BFLFBC841.1 CUV 1(3)Dip.f.m.4661/CFLFBCFLFBCChanson de Bertrand du Guesclin568501UNINA02754nam 2200589 450 991015309560332120230803220238.01-292-03844-6(CKB)2550000001126668(SSID)ssj0001256733(PQKBManifestationID)12563384(PQKBTitleCode)TC0001256733(PQKBWorkID)11272818(PQKB)11229157(MiAaPQ)EBC5174471(MiAaPQ)EBC5187054(MiAaPQ)EBC5833371(MiAaPQ)EBC5138675(MiAaPQ)EBC6399542(Au-PeEL)EBL5138675(CaONFJC)MIL527331(OCoLC)1024279327(EXLCZ)99255000000112666820210324d2014 uy 0engurcnu||||||||txtccrThe art and science of Java an introduction to computer science /Eric RobertsPearson New International edition.Harlow, England :Pearson,[2014]©20141 online resource (555 pages) color illustrations, photographsAlways LearningIncludes index.1-292-02603-0 1-299-96080-4 Cover -- Table of Contents -- Chapter 1. Introduction -- Chapter 2. Programming by Example -- Chapter 3. Expressions -- Chapter 4. Statement Forms -- Chapter 5. Statement Forms -- Chapter 6. Objects and Classes -- Chapter 7. Objects and Memory -- Chapter 8. Strings and Characters -- Chapter 9. Object-oriented Graphics -- Chapter 10. Event-driven Programs -- Chapter 11. Arrays and ArrayLists -- Chapter 12. Searching and Sorting -- Chapter 13. Collection Classes -- Index.In The Art and Science of Java, Stanford professor and well-known leader in CS Education Eric Roberts emphasizes the student-friendly exposition that led to the success of The Art and Science of C. By following the recommendations of the Association of Computing Machinery's Java Task Force, this first edition text adopts a modern objects-first approach that introduces students to useful hierarchies from the very beginning. Packages are translated into a minimally complex collection of pedagogical resources that make it easier to teach Java while retaining the language's industrial strength.Always learning.Java (Computer program language)Java (Computer program language)005.133Roberts Eric66466MiAaPQMiAaPQMiAaPQBOOK9910153095603321The art and science of Java3414699UNINA06948nam 2200589 450 99649516710331620230809170104.09783031142055(electronic bk.)9783031142048(MiAaPQ)EBC7127772(Au-PeEL)EBL7127772(CKB)25219376900041(PPN)26585640X(EXLCZ)992521937690004120230317d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMeasure theory, probability, and stochastic processes /Jean-François Le GallCham, Switzerland :Springer,[2022]©20221 online resource (409 pages)Graduate texts in mathematics ;Volume 295Print version: 9783031142048 Includes bibliographical references (pages 401-402) and index.Intro -- Preface -- Contents -- List of Symbols -- Part I Measure Theory -- 1 Measurable Spaces -- 1.1 Measurable Sets -- 1.2 Positive Measures -- 1.3 Measurable Functions -- Operations on Measurable Functions -- 1.4 Monotone Class -- 1.5 Exercises -- 2 Integration of Measurable Functions -- 2.1 Integration of Nonnegative Functions -- 2.2 Integrable Functions -- 2.3 Integrals Depending on a Parameter -- 2.4 Exercises -- 3 Construction of Measures -- 3.1 Outer Measures -- 3.2 Lebesgue Measure -- 3.3 Relation with Riemann Integrals -- 3.4 A Subset of ps: [/EMC pdfmark [/Subtype /Span /ActualText (double struck upper R) /StPNE pdfmark [/StBMC pdfmarkRps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark Which Is Not Measurable -- 3.5 Finite Measures on ps: [/EMC pdfmark [/Subtype /Span /ActualText (double struck upper R) /StPNE pdfmark [/StBMC pdfmarkRps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark and the Stieltjes Integral -- 3.6 The Riesz-Markov-Kakutani Representation Theorem -- 3.7 Exercises -- 4 Lp Spaces -- 4.1 Definitions and the Hölder Inequality -- 4.2 The Banach Space ps: [/EMC pdfmark [/Subtype /Span /ActualText (upper L Superscript p Baseline left parenthesis upper E comma script upper A comma mu right parenthesis) /StPNE pdfmark [/StBMC pdfmarkLp(E,A,μ)ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 4.3 Density Theorems in Lp Spaces -- 4.4 The Radon-Nikodym Theorem -- 4.5 Exercises -- 5 Product Measures -- 5.1 Product σ-Fields -- 5.2 Product Measures -- 5.3 The Fubini Theorems -- 5.4 Applications -- 5.4.1 Integration by Parts -- 5.4.2 Convolution -- 5.4.3 The Volume of the Unit Ball -- 5.5 Exercises -- 6 Signed Measures -- 6.1 Definition and Total Variation -- 6.2 The Jordan Decomposition -- 6.3 The Duality Between Lp and Lq -- 6.4 The Riesz-Markov-Kakutani Representation Theorem for Signed Measures -- 6.5 Exercises.7 Change of Variables -- 7.1 The Change of Variables Formula -- 7.2 The Gamma Function -- 7.3 Lebesgue Measure on the Unit Sphere -- 7.4 Exercises -- Part II Probability Theory -- 8 Foundations of Probability Theory -- 8.1 General Definitions -- 8.1.1 Probability Spaces -- 8.1.2 Random Variables -- 8.1.3 Mathematical Expectation -- 8.1.4 An Example: Bertrand's Paradox -- 8.1.5 Classical Laws -- 8.1.6 Distribution Function of a Real Random Variable -- 8.1.7 The σ-Field Generated by a Random Variable -- 8.2 Moments of Random Variables -- 8.2.1 Moments and Variance -- 8.2.2 Linear Regression -- 8.2.3 Characteristic Functions -- 8.2.4 Laplace Transform and Generating Functions -- 8.3 Exercises -- 9 Independence -- 9.1 Independent Events -- 9.2 Independence for σ-Fields and Random Variables -- 9.3 The Borel-Cantelli Lemma -- 9.4 Construction of Independent Sequences -- 9.5 Sums of Independent Random Variables -- 9.6 Convolution Semigroups -- 9.7 The Poisson Process -- 9.8 Exercises -- 10 Convergence of Random Variables -- 10.1 The Different Notions of Convergence -- 10.2 The Strong Law of Large Numbers -- 10.3 Convergence in Distribution -- 10.4 Two Applications -- 10.4.1 The Convergence of Empirical Measures -- 10.4.2 The Central Limit Theorem -- 10.4.3 The Multidimensional Central Limit Theorem -- 10.5 Exercises -- 11 Conditioning -- 11.1 Discrete Conditioning -- 11.2 The Definition of Conditional Expectation -- 11.2.1 Integrable Random Variables -- 11.2.2 Nonnegative Random Variables -- 11.2.3 The Special Case of Square Integrable Variables -- 11.3 Specific Properties of the Conditional Expectation -- 11.4 Evaluation of Conditional Expectation -- 11.4.1 Discrete Conditioning -- 11.4.2 Random Variables with a Density -- 11.4.3 Gaussian Conditioning -- 11.5 Transition Probabilities and Conditional Distributions -- 11.6 Exercises.Part III Stochastic Processes -- 12 Theory of Martingales -- 12.1 Definitions and Examples -- 12.2 Stopping Times -- 12.3 Almost Sure Convergence of Martingales -- 12.4 Convergence in Lp When p&gt -- 1 -- 12.5 Uniform Integrability and Martingales -- 12.6 Optional Stopping Theorems -- 12.7 Backward Martingales -- 12.8 Exercises -- 13 Markov Chains -- 13.1 Definitions and First Properties -- 13.2 A Few Examples -- 13.2.1 Independent Random Variables -- 13.2.2 Random Walks on ps: [/EMC pdfmark [/Subtype /Span /ActualText (double struck upper Z Superscript d) /StPNE pdfmark [/StBMC pdfmarkZdps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark -- 13.2.3 Simple Random Walk on a Graph -- 13.2.4 Galton-Watson Branching Processes -- 13.3 The Canonical Markov Chain -- 13.4 The Classification of States -- 13.5 Invariant Measures -- 13.6 Ergodic Theorems -- 13.7 Martingales and Markov Chains -- 13.8 Exercises -- 14 Brownian Motion -- 14.1 Brownian Motion as a Limit of Random Walks -- 14.2 The Construction of Brownian Motion -- 14.3 The Wiener Measure -- 14.4 First Properties of Brownian Motion -- 14.5 The Strong Markov Property -- 14.6 Harmonic Functions and the Dirichlet Problem -- 14.7 Harmonic Functions and Brownian Motion -- 14.8 Exercises -- A A Few Facts from Functional Analysis -- Normed Linear Spaces and Banach Spaces -- Hilbert Spaces -- Notes and Suggestions for Further Reading -- References -- Index.Graduate texts in mathematics ;Volume 295.Measure theoryProbabilitiesStochastic processesTeoria de la mesurathubProbabilitatsthubProcessos estocàsticsthubLlibres electrònicsthubMeasure theory.Probabilities.Stochastic processes.Teoria de la mesuraProbabilitatsProcessos estocàstics515.42Le Gall J. F(Jean-François),348889MiAaPQMiAaPQMiAaPQ996495167103316Measure Theory, Probability, and Stochastic Processes2963436UNISA