**Probability and Statistics: A Course for Physicists and Engineers**

by Arak M. Mathai, Hans J. Haubold

**Publisher**: De Gruyter Open 2017**ISBN-13**: 9783110562545**Number of pages**: 582

**Description**:

This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and scientists, the book also covers applied topics such as model building and experiment design.

Download or read it online for free here:

**Download link**

(multiple formats)

## Similar books

**Introduction to Randomness and Statistics**

by

**Alexander K. Hartmann**-

**arXiv**

This is a practical introduction to randomness and data analysis, in particular in the context of computer simulations. At the beginning, the most basics concepts of probability are given, in particular discrete and continuous random variables.

(

**11106**views)

**Probability and Mathematical Statistics**

by

**Prasanna Sahoo**-

**University of Louisville**

This book is an introduction to probability and mathematical statistics intended for students already having some elementary mathematical background. It is intended for a one-year junior or senior level undergraduate or beginning graduate course.

(

**6870**views)

**Reversible Markov Chains and Random Walks on Graphs**

by

**David Aldous, James Allen Fill**-

**University of California, Berkeley**

From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; etc.

(

**11263**views)

**Random Matrix Models and Their Applications**

by

**Pavel Bleher, Alexander Its**-

**Cambridge University Press**

The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems.

(

**13349**views)