Metropolis algorithm, Markov chain Monte

Adaptive Markov Chain Monte Carlo Simulation 2.5.3.1 Metropolis-Hastings Algorithm... 

The choice of poses is made with a traditional Metropolis approach. Metropolis algorithm is a Markov chain Monte Carlo method for obtaining a sequence of random poses from a probability distribution for which direct sampling is difficult. When the energy results are to be higher, the new conformation will be accepted or rejected if an acceptance probability law... 

Thus, we take advantage of the accuracy, robustness and efficiency of the direct problem solution, to tackle the associated inverse heat transfer problem analysis [26, 27] towards the simultaneous estimation of momentum and thermal accommodation coefficients in micro-channel flows with velocity slip and temperature jump. A Bayesian inference approach is adopted in the solution of the identification problem, based on the Monte Carlo Markov Chain method (MCMC) and the Metropolis-Hastings algorithm [28-30]. Only simulated temperature measurements at the external faces of the channel walls, obtained for instance via infrared thermography [30], are used in the inverse analysis in order to demonstrate the capabilities of the proposed approach. A sensitivity analysis allows for the inspection of the identification problem behavior when the external wall Biot number is also included among the parameters to be estimated. 

The most commonly used Monte Carlo method with Markov Chain algorithms are the Metropolis-Hastings, here employed, and the Gibbs sampler [28, 29]. 

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