Asmussen and Glynn - Stochastic Simulation

Athreya and Lahiri - Measure Theory and Probability Theory

Barbour - An Introduction to Stein's Method

Billingsley - Probability and Measure

Borodin - Handbook of Brownian Motion - Facts and Formulae

Borovkov - Probability Theory

Borovkov - Ergodicity and Stability of Stochastic Processes

Shiryaev - Probability I, II

Pitman - Probability

KL Chung - A Course in Probability Theory

KL Chung - Markov Chains with stationary transition probabilities

KL Chung - Markov Processes, Brownian Motion and Time Symmetry

KL Chung - Lectures from Markov Processes to Brownian Motion

Bremaud - Probability Theory and Stochastic Processes

Cinlar - Probability and Stochastics

Norris - Markov chains

Grimmett and Stirzaker - Probability and Random Processes

Revuz and Yor - Continuous Martingales and Brownian Motion

Schilling and Partzsch - Brownaian Motion

Mansuy, Yor - Aspects of Brownian Motion

LeGall - Brownian Motion, Martingales and Stochastic Calculus

Bhattacharaya - Random Walk, Brownian Motion and Martingales

Hall and Heyde - Martingale Limit Theory and its Applications

Ikeda, Watanabe - Ito's Stochastic Calculus and Probability Theory

Ito, McKean - Diffusion Processes and their Sample Paths

Jacod, Shiryaev - Limit Theorems for Stochastic Processes

Kallenberg - Foundations of Modern Probability

Mansuy,Yor - Aspects of Brownian Motion

LeGall - Brownian MOtion, Martingales and Stochastic Calculus

Dobrow - Introduction to Stochastic Processes with R

Bhattacharya, Waymire - Random Walk, Brownian Motion and Martingales

Loffler, Kruschwitz - The Brownian Motion

Shiryaev - Problems in Probability

Mills - Problems in Probability

Chaumont and Yor - Exercises in Probability

Gusak - Problems in Stochastic Processes

Lemieux - Monte Carlo and Quasi-Monte Carlo Sampling

Okten - Probability and Simulation

Owen, Glynn - Monte Carlo and Quasi-Monte Carlo Methods (MCQMC 2016)

Adler - An introduction to continuity, extrema and related topics for general Gaussian processes

Rasmussen and Willaims - Gaussian Processes for Machine Learning

Santner - The Design and Analysis of Computer Experiments

Lifshits - Lectures on Gaussian Processes

Gramacy - Surrogates, Gaussian Process Modeling

Marcus - Markov Processes, Gaussian Processes and Local Times

Shi - Gaussian Process Regression Analysis for Functional Data

Bobrowski - Functional Analysis for Probability and Stochastic Processes

Ibragimov, Rozanov - Gaussian Random Processes

Wikle, Cressie - Spatio-Temporal Statistics with R

Bishwal - Parameter Estimation in Stochastic Differential Equatios

Horn and Johnson - Matrix Analysis

Gentle - Matrix Algebra

Golub and Loan - Matrix Computations

Graybill - Matrices with Applications in Statistics

Gruber - Matrix Algebra for Linear Models

Ravishanker and Dey - A First course in linear model theory

Searle - Linear Models

Rencher - Linear Models in Statistics

Faraway - Linear Models with R

Seber - Linear Regression Analysis

Seber - The Linear Model and Hypothesis - A general unifying theory

Hocking - Methods and Applications of Linear Models

Arnold - The Theory of Linear Models and Multivariate Analysis

Graybill - Theory and Application of the Linear Model

Faraway - Extending the Linear Model with R

Hosmer, Lemeshow, Sturdivant - Applied Logistic Regression

Kleinbaum, Klein - Logistic Regression

G. Vallis : Atmospheric and Oceanic Fluid Dynamics : Fundamentals and Large Scale Circulation

Bennett - Inverse Problams

Pedlosky - Geophysical Fluid Dynamics