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Monte Carlo algorithms often depend on Markov chains to sample from very large data sets. A key ingredient in the design of an efficient Markov chain is determining rigorous bounds on how quickly the chain "mixes," or converges, to its stationary distribution. This survey provides an overview of several useful techniques.