nonstandard analysis, a powerful machinery derived from mathematical logic, has had many applications in probability theory as well as stochastic processes. nonstandard analysis allows construction of a single object - a hyperfinite probability space - which satisfies all the first order logical properties of a finite probability space, but which can be simultaneously viewed as a measure-theoretical probability space via the loeb construction. as a consequence, the hyperfinite/measure duality has proven to be particularly useful in porting discrete results into their continuous settings. in this talk, for every general-state-space continuous-time markov process satisfying appropriate conditions, we construct a hyperfinite markov process which has all the basic order logical properties of a finite markov process to represent it. we show that the mixing time and the hitting time agree with each other up to some multiplicative constants for discrete-time general-state-space reversible markov processes satisfying certain condition. finally, we show that our result is applicable to a large class of gibbs samplers and metropolis-hasting algorithms.