Markov chains on disordered media and their scaling limits

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We analysed Markov chains on disordered media and their scaling limits in a unified manner by using probabilistic and analytic methods. From the viewpoint of constructing general theory, we established the following results; i) heat kernel estimates for non-symmetric Markov chains satisfying some cycle condition and analyzing their scaling limits, ii) equivalence of the sub-Gaussian heat kernel estimates and generalized Parabolic Harnack inequalities on symmetric diffusions for general metric measure spaces, iii) convergence of jump-type processes for general metric measure spaces. From the viewpoint of concrete examples, we established the following results; i) asymptotic behavior and scaling limits of biased random walks on critical percolation clusters (conditioned to survive forever) on trees, ii) convergence of scaled mixing times on Markov chains on random finite graphs.