**Joseph K. Blitzstein, Jessica Hwang**

February 08, 2019

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google...

**Siva Athreya, V. S. Sunder**

February 07, 2019

This book covers the fundamentals of measure theory and probability theory. It begins with the construction of Lebesgue measure via Caratheodory’s outer measure approach and goes on to discuss integration and standard convergence theorems and contains an entire chapter devoted to complex measures,...

**Arup Bose, Koushik Saha**

October 25, 2018

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the properties of the eigenvalues for various types of circulant matrices, such as the usual circulant, the reverse circulant, and the k-circulant when the dimension of the...

**Saeed Ghahramani**

September 04, 2018

"The 4th edition of Ghahramani's book is replete with intriguing historical notes, insightful comments, and well-selected examples/exercises that, together, capture much of the essence of probability. Along with its Companion Website, the book is suitable as a primary resource for a first course in...

**Arup Bose, Monika Bhattacharjee**

July 03, 2018

Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites...

**Joel A. Nachlas**

June 28, 2018

Suitable for a first course in probability theory and designed specifically for industrial engineering and operations management students, Probability Foundations for Engineers covers theory in an accessible manner and includes numerous practical examples based on engineering applications....

**Arup Bose**

May 17, 2018

Large dimensional random matrices (LDRM) with specific patterns arise in econometrics, computer science, mathematics, physics, and statistics. This book provides an easy initiation to LDRM. Through a unified approach, we investigate the existence and properties of the limiting spectral distribution...

**Ephraim Suhir**

April 05, 2018

Improvements in safety in the air and in space can be achieved through better ergonomics, better work environments, and other efforts of traditional avionic psychology that directly affect human behaviors and performance. Not limited to just the aerospace field, this book discusses adaptive...

**David A. Harville**

March 13, 2018

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models...

**Vinod Krishnamoorthy**

March 06, 2018

The 39 self-contained sections in this book present worked-out examples as well as many sample problems categorized by the level of difficulty as Bronze, Silver, and Gold in order to help the readers gauge their progress and learning. Detailed solutions to all problems in each section are provided...

**Rodrigo A. Collazo, Christiane Goergen, Jim Q. Smith**

January 30, 2018

Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of...

**Lyle D. Broemeling**

December 15, 2017

This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples...