FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title Probability and Mathematical Statistics
 Author(s) Prasanna Sahoo
 Publisher: University of Louisville
 Paperback N/A
 eBook PDF (712 pages), ePub, and Kindle, etc.
 Language: English
 ISBN10/ASIN: N/A
 ISBN13: N/A
 Share This:
Book Description
This book presents an introduction to probability and mathematical statistics and it is intended for students already having some elementary mathematical background.
It is both a tutorial and a textbook, blends proven coverage with new innovations to ensure you gain a solid understanding of statistical concepts  and see their relevance to your everyday life.
With this book, you will be able to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests, and know what to do when statistical assumptions have been violated.
The book contains more material than normally would be taught in a oneyear course. This should give the teacher flexibility with respect to the selection of the content and level at which the book is to be used. This book is based on over 15 years of lectures in senior level calculus based courses in probability theory and mathematical statistics at the University of Louisville.
About the Authors N/A
 Probability, Stochastic Process, Queueing Theory, etc.
 Statistics, Mathematical Statistics, and SAS Programming
 Physics, Computational Physics, and Mathematical Physics
 Probability and Mathematical Statistics (Prasanna Sahoo)
 The Mirror Site (1)  PDF
 The Mirror Site (2)  PDF

Introduction to Probability for Data Science (Stanley Chan)
This book is an introductory textbook in undergraduate probability in the context of data science to emphasize the inseparability between data (computing) and probability (theory) in our time, with examples in both MATLAB and Python.

Applied Probability (Paul Pfeiffer)
This book presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the real world application in industries and science.

Introduction to Probability, Statistics, and Random Processes
This book introduces students to probability, statistics, and stochastic processes. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy. The book contains a large number of solved exercises.

Foundations of Constructive Probability Theory (YuenKwok Chan)
This book provides a systematic and general theory of probability within the framework of Constructive Mathematics. It can serve as a parallel introduction into constructive mathematics and rigorous probability theory.

Introduction to Random Matrices: Theory and Practice
This is a book for absolute beginners. The aim is to provide a truly accessible introductory account of Random Matrix theory. Most chapters are accompanied by MATLAB codes to guide readers through the numerical check of most analytical results.

Elementary Probability for Applications (Rick Durrett)
This clear and lively introduction to probability theory concentrates on the results that are the most useful for applications, including combinatorial probability and Markov chains. Concise and focused, it is designed for students familiar with basic calculus.

Essentials of Stochastic Processes (Rick Durrett)
Stochastic processes have become important for many fields, including mathematical finance and engineering. Written by one of the worlds leading probabilists, this book presents recent results previously available only in specialized monographs.

Introduction to Modern Statistics (Mine Ã‡etinkayaRundel, et al.)
This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulationbased inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.

Foundations in Statistical Reasoning (Pete Kaslik)
This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.

Theory of Statistics (James E. Gentle)
This book is directed toward students for whom mathematical statistics is or will become an important part of their lives. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics.

Introduction to Statistical Learning: with Applications in Python
This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

O'Reilly® Think Bayes: Bayesian Statistics in Python
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.
:






















