Abstract In the current work we present two generalizations of Parallel Tempering algorithm in context discrete-time Markov chain Monte Carlo methods for Bayesian inverse problems. These use state-dependent swapping rates, inspired by so-called continuous time Infinite Swapping presented Plattner et al. (J Chem Phys 135(13):134111, 2011). We analyze reversibility and ergodicity properties our g...

The tempered martensite lath structure of a 10% Cr martensitic steel after normalizing and tempering at 750 °C was studied. effect reducing the temperature from 770 by 20 on microstructural parameters in shown. Tempering lower provides thinner laths finer M23C6 particles (55 nm) than °C. Such structural changes do not affect creep rupture time 650 applied stresses 180 160 MPa, whereas positive ...

The aim of this research is the reactivation secondhand ((scrap)) steel compression springs, which used in one private plastic factory, by applying heat - treatment with three methods. 1- Quenching only. 2- and tempering. 3- Annealing , quenching tempering.Were doing under supply conditions. microstructures inspected for specimen from any model 500 X. hardness tests springs have done before aft...

In this work, we use a tempering-based adaptive particle filter to infer from partially observed stochastic rotating shallow water (SRSW) model which has been derived using the Stochastic Advection by Lie Transport (SALT) approach. The methodology present here validates applicability of tempering and sample regeneration Metropolis-Hastings procedure high-dimensional models appearing in geophysi...

The parallel-tempering Monte Carlo method is used in combination with a Drude model to characterize the (H2O)6 2 cluster over the 50–190 K temperature range. Chainlike structures are found to account for about 50% of the population at 190 K, whereas they are unimportant at the temperatures below about 130 K. At the lowest temperature considered, prismlike structures are dominant. Two new low-en...

We propose a few remedies to improve training of Gaussian-Bernoulli restricted Boltzmann machines (GBRBM), which is known to be difficult. Firstly, we use a different parameterization of the energy function, which allows for more intuitive interpretation of the parameters and facilitates learning. Secondly, we propose parallel tempering learning for GBRBM. Lastly, we use an adaptive learning ra...

REPLICA EXCHANGE MONTE CARLO SIMULATIONS OF THE ISING SPIN GLASS: STATIC AND DYNAMIC PROPERTIES