Complex problems that cross traditional disciplinary lines between physics, chemistry, biology, and materials science can be studied at an unprecedented level of detail using increasingly sophisticated theoretical methodology and high-speed computing platforms. The tools of statistical mechanics provide the bridge between the atomistic descriptions of these complex systems and the macroscopic observables accessible to experimental investigations and predictable in computer simulations. The aim of this book is to prepare burgeoning users and developers to become active researchers in the theoretical and computational molecular sciences by uniting, in one monograph, the theoretical underpinnings of equilibrium and time-dependent classical and quantum statistical mechanics with modern computational techniques used to put these concepts into practice to address real-world applications. The book contains detailed reviews of classical and quantum mechanics and in-depth discussions of the most commonly used statistical ensembles side by side with modern computational methods such as molecular dynamics, Monte Carlo, advanced configurational and trajectory sampling approaches, free-energy based rare-event sampling approaches, Feynman path integral techniques, linear response theory and time correlation functions, stochastic methods, critical phenomena, and an introduction to machine learning and its uses in statistical mechanics. Readers of this book will be provided, in a pedagogical manner, with a firm foundation in both the theory and practical implementation of statistical mechanical concepts, thus allowing them to approach application technology with an understanding of the underlying algorithms and to become, themselves, creators of new and powerful approaches for solving challenging research problems"--