Books (by topic)

Asmussen and Glynn - Stochastic Simulation

Probability and Stochastic Processes
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

Problems in Probability
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