{"product_id":"probability-concepts-and-theory-for-engineers-9780470748558","title":"Probability Concepts and Theory for Engineers","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book aims to get the electrical and electronic engineering student well-versed in the machinery of probability theory. It steers clear of getting into application areas any more than is needed to get the reader comfortable with the mathematics and connecting it to models of practical situations.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"After reading some introductory material on conventions and notions, it is possible to use separate chapters as introductions to various ideas. This is how readers should use this book.\" (Computing Reviews, 1 October 2011)\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I. The Basic Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart I Introduction.\u003c\/p\u003e \u003cp\u003eSection 1. Dealing with ‘Real-World’ Problems.\u003c\/p\u003e \u003cp\u003eSection 2. The Probabilistic Experiment.\u003c\/p\u003e \u003cp\u003eSection 3. Outcome.\u003c\/p\u003e \u003cp\u003eSection 4. Events.\u003c\/p\u003e \u003cp\u003eSection 5. The Connection to the Mathematical World.\u003c\/p\u003e \u003cp\u003eSection 6. Elements and Sets.\u003c\/p\u003e \u003cp\u003eSection 7. Classes of Sets.\u003c\/p\u003e \u003cp\u003eSection 8. Elementary Set Operations.\u003c\/p\u003e \u003cp\u003eSection 9. Additional Set Operations.\u003c\/p\u003e \u003cp\u003eSection 10. Functions.\u003c\/p\u003e \u003cp\u003eSection 11. The Size of a Set.\u003c\/p\u003e \u003cp\u003eSection 12. Multiple and Infinite Set Operations.\u003c\/p\u003e \u003cp\u003eSection 13. More About Additive Classes.\u003c\/p\u003e \u003cp\u003eSection 14. Additive Set Functions.\u003c\/p\u003e \u003cp\u003eSection 15. More about Probabilistic Experiments.\u003c\/p\u003e \u003cp\u003eSection 16. The Probability Function.\u003c\/p\u003e \u003cp\u003eSection 17. Probability Space.\u003c\/p\u003e \u003cp\u003eSection 18. Simple Probability Arithmetic.\u003c\/p\u003e \u003cp\u003ePart I Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II. The Approach to Elementary Probability Problems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart II. Introduction.\u003c\/p\u003e \u003cp\u003eSection 19. About Probability Problems.\u003c\/p\u003e \u003cp\u003eSection 20. Equally Likely Possible Outcomes.\u003c\/p\u003e \u003cp\u003eSection 21. Conditional Probability.\u003c\/p\u003e \u003cp\u003eSection 22. Conditional Probability Distributions.\u003c\/p\u003e \u003cp\u003eSection 23. Independent Events.\u003c\/p\u003e \u003cp\u003eSection 24. Classes of Independent Events.\u003c\/p\u003e \u003cp\u003eSection 25. Possible Outcomes Represented as Ordered k-Tuples.\u003c\/p\u003e \u003cp\u003eSection 26. Product Experiments and Product Spaces.\u003c\/p\u003e \u003cp\u003eSection 27. Product Probability Spaces.\u003c\/p\u003e \u003cp\u003eSection 28. Dependence Between the Components in an Ordered k-Tuple.\u003c\/p\u003e \u003cp\u003eSection 29. Multiple Observations Without Regard to Order.\u003c\/p\u003e \u003cp\u003eSection 30. Unordered Sampling with Replacement.\u003c\/p\u003e \u003cp\u003eSection 31. More Complicated Discrete Probability Problems.\u003c\/p\u003e \u003cp\u003eSection 32. Uncertainty and Randomness.\u003c\/p\u003e \u003cp\u003eSection 33. Fuzziness.\u003c\/p\u003e \u003cp\u003ePart II Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III. Introduction to Random Variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart III. Introduction.\u003c\/p\u003e \u003cp\u003eSection 34. Numerical-Valued Outcomes.\u003c\/p\u003e \u003cp\u003eSection 35. The Binomial Distribution.\u003c\/p\u003e \u003cp\u003eSection 36. The Real Numbers.\u003c\/p\u003e \u003cp\u003eSection 37. General Definition of a Random Variable.\u003c\/p\u003e \u003cp\u003eSection 38. The Cumulative Distribution Function.\u003c\/p\u003e \u003cp\u003eSection 39. The Probability Density Function.\u003c\/p\u003e \u003cp\u003eSection 40. The Gaussian Distribution.\u003c\/p\u003e \u003cp\u003eSection 41. Two Discrete Random Variables.\u003c\/p\u003e \u003cp\u003eSection 42. Two Arbitrary Random Variables.\u003c\/p\u003e \u003cp\u003eSection 43. Two-Dimensional Distribution Functions.\u003c\/p\u003e \u003cp\u003eSection 44. Two-Dimensional Density Functions.\u003c\/p\u003e \u003cp\u003eSection 45. Two Statistically Independent Random Variables.\u003c\/p\u003e \u003cp\u003eSection 46. Two Statistically Independent Random Variables-Absolutely Continuous Case.\u003c\/p\u003e \u003cp\u003ePart III Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV. Transformations and Multiple Random Variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart IV Introduction.\u003c\/p\u003e \u003cp\u003eSection 47. Transformation of a Random Variable.\u003c\/p\u003e \u003cp\u003eSection 48. Transformation of a Two-Dimensional Random Variable.\u003c\/p\u003e \u003cp\u003eSection 49. The Sum of Two Discrete Random Variables.\u003c\/p\u003e \u003cp\u003eSection 50. The Sum of Two Arbitrary Random Variables.\u003c\/p\u003e \u003cp\u003eSection 51. n-Dimensional Random  Variables.\u003c\/p\u003e \u003cp\u003eSection 52. Absolutely Continuous n-Dimensional R. V.’s.\u003c\/p\u003e \u003cp\u003eSection 53. Coordinate Transformations.\u003c\/p\u003e \u003cp\u003eSection 54. Rotations and the Bivariate Gaussian Distribution.\u003c\/p\u003e \u003cp\u003eSection 55. Several Statistically Independent Random Variables.\u003c\/p\u003e \u003cp\u003eSection 56. Singular Distributions in One Dimension.\u003c\/p\u003e \u003cp\u003eSection 57. Conditional Induced Distribution, Given an Event.\u003c\/p\u003e \u003cp\u003eSection 58. Resolving a Distribution into Components of Pure Type.\u003c\/p\u003e \u003cp\u003eSection 59. Conditional Distribution Given the Value of a Random Variable.\u003c\/p\u003e \u003cp\u003eSection 60. Random Occurrences in Time.\u003c\/p\u003e \u003cp\u003ePart IV Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V. Parameters for Describing Random Variables and Induced Distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 61. Some Properties of a Random Variable.\u003c\/p\u003e \u003cp\u003eSection 62. Higher Moments.\u003c\/p\u003e \u003cp\u003eSection 63. Expectation of a Function of a Random Variable.\u003c\/p\u003e \u003cp\u003eSection 64. The Variance of a Function of a Random Variable.\u003c\/p\u003e \u003cp\u003eSection 65. Bounds on the Induced Distribution.\u003c\/p\u003e \u003cp\u003eSection 66. Test Sampling.\u003c\/p\u003e \u003cp\u003eSection 67. Conditional Expectation with Respect to an Event.\u003c\/p\u003e \u003cp\u003eSection 68. Covariance and Correlation Coefficient.\u003c\/p\u003e \u003cp\u003eSection 69. The Correlation Coefficient as Parameter in a Joint Distribution.\u003c\/p\u003e \u003cp\u003eSection 70. More General Kinds of Dependence Between Random Variables.\u003c\/p\u003e \u003cp\u003eSection 71. The Covariance Matrix.\u003c\/p\u003e \u003cp\u003eSection 72. Random Variables as the Elements of a Vector Space.\u003c\/p\u003e \u003cp\u003eSection 73. Estimation.\u003c\/p\u003e \u003cp\u003eSection 74. The Stieltjes Integral.\u003c\/p\u003e \u003cp\u003ePart V Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI. Further Topics in Random Variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePart VI Introduction.\u003c\/p\u003e \u003cp\u003eSection 75. Complex Random Variables.\u003c\/p\u003e \u003cp\u003eSection 76. The Characteristic Function.\u003c\/p\u003e \u003cp\u003eSection 77. Characteristic Function of a Transformed Random Variable.\u003c\/p\u003e \u003cp\u003eSection 78. Characteristic Function of a Multidimensional Random Variable.\u003c\/p\u003e \u003cp\u003eSection 79. The Generating Function.\u003c\/p\u003e \u003cp\u003eSection 80. Several Jointly Gaussian Random Variables.\u003c\/p\u003e \u003cp\u003eSection 81. Spherically Symmetric Vector Random Variables.\u003c\/p\u003e \u003cp\u003eSection 82. Entropy Associated with Random Variables.\u003c\/p\u003e \u003cp\u003eSection 83. Copulas.\u003c\/p\u003e \u003cp\u003eSection 84. Sequences of Random Variables.\u003c\/p\u003e \u003cp\u003eSection 85. Convergent Sequences and Laws of Large Numbers.\u003c\/p\u003e \u003cp\u003eSection 86. Convergence of Probability Distributions and the Central Limit Theorem.\u003c\/p\u003e \u003cp\u003ePart VI Summary.\u003c\/p\u003e \u003cp\u003eAppendices.\u003c\/p\u003e \u003cp\u003eNotation and Abbreviations.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eSubject Index.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":52090689388887,"sku":"9780470748558","price":66.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470748558.jpg?v=1762273097","url":"https:\/\/bookcurl.com\/products\/probability-concepts-and-theory-for-engineers-9780470748558","provider":"Book Curl","version":"1.0","type":"link"}