Principles of Probability Theory and its Applications by Majid Eyvazian (PDF)

Description

Audience:Students in higher education (B.Sc. or M.Sc.) and their teachers involved in probability theory and its applicationsMotivation:Many students have been experiencing a hard time dealing with intertwined subjects in probability theory. In fact, they cannot progress in understanding probability theory while studying it much because they have found themselves impotent in coping with probability theory’ complex and interwoven concepts. In this regard, we have come up with the following ideas to be considered as our main contributions:• Breaking down the concepts and materials into 11 chapters, each of which delves into a different aspect of probability theory.• Presenting different problems in each chapter section to help students learn concepts more effectively while experiencing increasing problem difficulty.• Explaining complex concepts in probability theory as simply as possible.• Providing fully-explained answers to each question to eliminate ambiguities that students may encounter and to ensure that they learn the topics taught in the text of each chapter if they do their best.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I have read the first 7 chapters and really enjoyed it so far.I got this book for a technical data science interview which I knew many hard probability problems were waiting for me. This book was way beyond those questions.As someone with experience in teaching probably and stat both in grad and undergrad, I believe this is a great book for both students and professors. The key pros of this book is ridiculous number of examples in each chapter- particularly helpful for students who struggle to differentiate between different types of probability problems. The book guides you through various interesting and sometimes challenging examples , each slightly different from one other, making it easy to grasp the concepts.Plus, there are an endless number of problems at the end of each chapter, making it a great source for instructors. It also has a reasonably priced solution manual which I haven’t bought yet.All in all, great book and highly recommended!

⭐There is a lot of groundwork laid out in this probability textbook. It begins with a description of counting principles and gradually progresses into probability. I am fascinated by the sequence of topics and materials. It begins with the premise that you know nothing, but it seems comprehensive.

⭐This is the best English-language introduction to probability that does not rely on measure theory. Many introductory probability texts, including Ross, Ghahremani, and Papoulis, have been thoroughly studied by me. This is the only book I feel confident recommending to someone who knows nothing but basic calculus.It doesn’t matter if you’re a freshman in college about to take your first probability course, a seasoned grad student brushing up on the subject, a high school teacher preparing to introduce probability to your students.

⭐I am a Data Science PhD student and this book was recommended by one of my co-advisors. This book provided me with very enough knowledge I needed for my data-driven project. For example, chapter 3 gave me the background needed for Bayesian classification and inference analysis. For another project, I used the expected value chapter (if I am not wrong I think was chapter 5). The examples in this book are so simple and at the same time deep enough that you thoroughly understand the concept and make a case for your project. I think this book can greatly help students with different levels of needs, either just basic understanding or a very deep one for thesis and problem definitions. I highly recommend this book for people who are majoring in any statistical learning-related field including and not limited to computer science, data science, data analytics, business analytics, machine learning, etc. You will enjoy reading the solution manual which is very clearly explained by the authors.

⭐Complete book in this price with full of practical examples for those who are looking for fundamentals in probability

⭐This is a great resource for either complete beginners or those who just need a refresher on the fundamentals of probability theory. The book avoids abstract definitions in favor of numerous examples, and it concludes with in-depth, worked-out questions, and answers to ensure that the reader feels as though he has a firm grasp of the material. Useful formulae not typically found in probability theory are cited and explained in depth so as not to assume the reader already possesses the requisite background knowledge.

⭐I bought this book as a data analytics enthusiast interested in probability and its applications. I am a Ph.D. student of Business Analytics, and my background is in industrial engineering. Based on my experience from reading several famous sources, such as Sheldon Ross’s “First Course on Probability” and Dimitri Bertsekas’s “Introduction to Probability,” I found this one as a combination of what you need to be a master in probability. The good news is the price which is about 1/4 of those mentioned. The book contains many examples, and the way it conveys the materials is smooth and understandable, even for junior students.I suggest this book to professors, grad and undergrad students in industrial engineering, mathematics, and computer science departments, or anyone interested in subjects like combinatorial analysis, conditional probability, discrete and continuous random variables. You will enjoy many great examples and exercises.

⭐As a Ph.D. student working on statistical analysis, I found it a great book.For starters, the statistical coverage is extensive enough for the would-be expert, and the book is also well-organized enough for those who prefer to skim only certain chapters.In addition, each chapter includes a variety of real-world examples that might help readers better comprehend the ideas presented there.In the end, I strongly suggest this book to college students and professors who want to learn more about statistics.

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