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"Roussas's Introduction to Probability features exceptionally clear explanations of the mathematics of probability theory and explores its diverse applications through numerous interesting and motivational examples. It provides a thorough introduction to the subject for professionals and advanced students taking their first course in probability. The content is based on the introductory chapters of Roussas's book, An Intoduction to Probability and Statistical Inference, with additional chapters and revisions. Well-respected author known for great exposition and readability Many real world ex."@en
"Probability models, statistical methods, and the information to be gained from them is vital for work in business, engineering, sciences (including social and behavioral), and other fields. Data must be properly collected, analyzed and interpreted in order for the results to be used with confidence. Roussas introduces readers with no prior knowledge in probability or statistics, to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving th."@en
""In this introductory chapter, the concepts of a field and of a [sigma]-field are introduced, they are illustrated bymeans of examples, and some relevant basic results are derived. Also, the concept of a monotone class is defined and its relationship to certain fields and [sigma]-fields is investigated. Given a collection of measurable spaces, their product space is defined, and some basic properties are established. The concept of a measurable mapping is introduced, and its relation to certain [sigma]-fields is studied. Finally, it is shown that any random variable is the pointwise limit of a sequence of simple random variables"--"@en
"Introduction to Probability, Second Edition, is written for upper-level undergraduate students in statistics, mathematics, engineering, computer science, operations research, actuarial science, biological sciences, economics, physics, and some of the social sciences. With his trademark clarity and economy of language, the author explains important concepts of probability, while providing useful exercises and examples of real world applications for students to consider. After introducing fundamental probability concepts, the book proceeds to topics including special distributions, the joint probability density function, covariance and correlation coefficients of two random variables, and more. Demonstrates the applicability of probability to many human activities with examples and illustrationsDiscusses probability theory in a mathematically rigorous, yet accessible wayEach section provides relevant proofs, and is followed by exercises and useful hintsAnswers to even-numbered exercises are provided and detailed answers to all exercises are available to instructors on the book companion site."@en
""In this introductory chapter, the concepts of a field and of a [sigma]-field are introduced, they are illustrated bymeans of examples, and some relevant basic results are derived.Also, the concept of a monotone class is defined and its relationship to certain fields and [sigma]-fields is investigated. Given a collection of measurable spaces, their product space is defined, and some basic properties are established. The concept of a measurable mapping is introduced, and its relation to certain [sigma]-fields is studied. Finally, it is shown that any random variable is the pointwise limit of a sequence of simple random variables"--"
"Probability models, statistical methods, and the information to be gained from them is vital for work in business, engineering, sciences (including social and behavioral), and other fields. Data must be properly collected, analyzed and interpreted in order for the results to be used with confidence.Roussas introduces readers with no prior knowledge in probability or statistics, to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. Contains more than 200 illustrative examples discussed in detail, plus scores of numerical examples and applications Chapters 1-8 can be used independently for an introductory course in probability Provides a substantial number of proofs."
"This book provides in a concise, yet detailed way, the bulk of the probabilistic tools that a student working toward an advanced degree in statistics, probability and other related areas, should be equipped with. The approach is classical, avoiding the use of mathematical tools not necessary for carrying out the discussions. All proofs are presented in full detail.* Excellent exposition marked by a clear, coherent and logical devleopment of the subject* Easy to understand, detailed discussion of material* Complete proofs."@en

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"Introduction to probability"@en
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"An introduction to measure-theoretic probability"
"An Introduction to Probability and Statistical Inference"@en
"An introduction to probability and statistical inference"@en
"An introduction to probability and statistical inference"

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