Books like Introduction to probability theory by Paul Gerhard Hoel

A comprehensive introductory text that explains probability theory with clear examples and challenging problem sets.

A concise primer on probability theory and stochastic models for science, engineering, economics and related disciplines.

The book is an outcome of many years of teaching probability theory to undergraduate students. The author crafted the text to cater to students with a basic mathematical background, aligning the content with the syllabi of Honours courses from various Indian universities. The book’s main goal is to serve as a comprehensive and accessible resource on probability theory. A variety of problems, mostly sourced from university question papers, are included to help students reinforce their understanding. Additionally, the book contains a set of miscellaneous examples at the end, designed to add further appeal and practical application.

Now in its new third edition, Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Retaining the unique approach of the previous editions, this text interweaves material on probability and measure, so that probability problems generate an interest in measure theory and measure theory is then developed and applied to probability. Probability and Measure provides thorough coverage of probability, measure, integration, random variables and expected values, convergence of distributions, derivatives and conditional probability, and stochastic processes. The Third Edition features an improved treatment of Brownian motion and the replacement of queuing theory with ergodic theory. Like the previous editions, this new edition will be well received by students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory. --back cover

A comprehensive guide to probability theory with applications in statistics and machine learning, suitable for graduate students and professionals alike.

A foundational guide for undergraduate students that introduces elementary probability theory and stochastic processes, and demonstrates their application across engineering, computer science, management science, and the physical and social sciences.

Revised edition of DeGroot’s classic, blending classical and Bayesian statistics with new chapters on simulation—MCMC and Bootstrap—and enhanced residual analysis for linear models, all illustrated with real‑world data.

A comprehensive guide to mathematical statistics for students and professionals alike, covering a wide range of topics in thirty-five chapters of varying sizes.