This paper studies the mixing time of certain adaptive Markov Chain Monte Carlo algorithms. Under some regularity conditions, we show that the convergence rate of Importance Resampling MCMC (IRMCMC) algorithm, measured in terms of the total variation distance is O(n−1), and by means of an example, we establish that in general, this algorithm does not converge at a faster rate. We also study the...

Temperature microstructure is mapped in two dimensions for a section 6.7 km long and 150 m deep in water which is 200 m deep. The section is located on the continental shelf southeast of Point Conception on the southern California coast. Regions of mixing activity are composed of numerous individual events, some of which are observed to collapse onto isopycnal surfaces when plotted in a distanc...

We investigate a class of Hamiltonian particle systems and their stochastic behaviors. Using both rigorous proof and numerical simulations, we show that the geometric configuration can qualitatively change key statistical characteristics of the particle system, which are expected to be retained by stochastic modifications. In particular, whether a particle system has an exponential mixing rate ...

This paper theoretically and experimentally investigates a micromixer based on combined hydrodynamic focusing and time-interleaved segmentation. Both hydrodynamic focusing and time-interleaved segmentation are used in the present study to reduce mixing path, to shorten mixing time, and to enhance mixing quality. While hydrodynamic focusing reduces the transversal mixing path, time-interleaved s...