xii PREFACE We will only give a taste of the applications to computer science and statistical physics our focus will be on the common underlying mathematics. The prerequi- sites are all at the undergraduate level. We will draw primarily on probability and linear algebra, but we will also use the theory of groups and tools from analysis when appropriate. Why should mathematicians study Markov chain convergence? First of all, it is a lively and central part of modern probability theory. But there are ties to several other mathematical areas as well. The behavior of the random walk on a graph reveals features of the graph’s geometry. Many phenomena that can be observed in the setting of finite graphs also occur in differential geometry. Indeed, the two fields enjoy active cross-fertilization, with ideas in each playing useful roles in the other. Reversible finite Markov chains can be viewed as resistor networks the resulting discrete potential theory has strong connections with classical potential theory. It is amusing to interpret random walks on the symmetric group as card shuffles—and real shuffles have inspired some extremely serious mathematics—but these chains are closely tied to core areas in algebraic combinatorics and representation theory. In the spring of 2005, mixing times of finite Markov chains were a major theme of the multidisciplinary research program Probability, Algorithms, and Statistical Physics, held at the Mathematical Sciences Research Institute. We began work on this book there. Overview We have divided the book into two parts. In Part I, the focus is on techniques, and the examples are illustrative and accessible. Chapter 1 defines Markov chains and develops the conditions necessary for the existence of a unique stationary distribution. Chapters 2 and 3 both cover examples. In Chapter 2, they are either classical or useful—and generally both we include accounts of several chains, such as the gambler’s ruin and the coupon collector, that come up throughout probability. In Chapter 3, we discuss Glauber dynamics and the Metropolis algorithm in the context of “spin systems.” These chains are important in statistical mechanics and theoretical computer science. Chapter 4 proves that, under mild conditions, Markov chains do, in fact, con- verge to their stationary distributions and defines total variation distance and mixing time, the key tools for quantifying that convergence. The techniques of Chapters 5, 6, and 7, on coupling, strong stationary times, and methods for lower bounding distance from stationarity, respectively, are central to the area. In Chapter 8, we pause to examine card shuffling chains. Random walks on the symmetric group are an important mathematical area in their own right, but we hope that readers will appreciate a rich class of examples appearing at this stage in the exposition. Chapter 9 describes the relationship between random walks on graphs and electrical networks, while Chapters 10 and 11 discuss hitting times and cover times. Chapter 12 introduces eigenvalue techniques and discusses the role of the re- laxation time (the reciprocal of the spectral gap) in the mixing of the chain. In Part II, we cover more sophisticated techniques and present several detailed case studies of particular families of chains. Much of this material appears here for the first time in textbook form.

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