An easy-to-implement form of the Metropolis Algorithm is described which, unlike most standard techniques, is well suited to sampling from multi-modal distributions on spaces with moderate numbers of dimensions (order ten) in environments typical of investigations into current constraints on Beyond-the-Standard-Model physics. The sampling technique makes use of pre-existing information (which c...

The word ‘Metropolis’ is a shorter version of the word ‘meterpolis’, itself derived from the Greek word ‘meter’ (mother) and polis (city, town). It is identified as the ‘center city’ which is more developed than other cities in terms of culture and economy (1). Hence, the metropolis is a depiction of hybridity and multi-layering; and while it is related to the city, it is not solely derived fro...

Background and objective: The location of facilities is of great importance in healthcare and is of interest to researchers due to its importance. In this regard, a large proportion of classic location-allocation models concentrate on solving problems in an exclusive environment (non-competitive), but this assumption is rarely true in reality. Methods: At...

Adaptive Metropolis-Hastings samplers use information obtained from previous draws to tune the proposal distribution. The tuning is carried out automatically, often repeatedly, and continues after the burn-in period. Because the resulting chain is not Markovian, adaptation needs to be done carefully to ensure convergence to the correct ergodic distribution. In this paper we distill recent theor...

It is illustrated for 4D SU(2) lattice gauge theory that sampling with a biased Metropolis scheme is essentially equivalent to using the heatbath algorithm. Only, the biased Metropolis method can also be applied when an efficient heatbath algorithm does not exist. Other cases for which the use of the biased Metropolis-heatbath algorithm is beneficial are briefly summarized.

We investigate the use of adaptive MCMC algorithms to automatically tune the Markov chain parameters during a run. Examples include the Adaptive Metropolis (AM) multivariate algorithm of Haario et al. (2001), Metropolis-within-Gibbs algorithms for non-conjugate hierarchical models, regionally adjusted Metropolis algorithms, and logarithmic scalings. Computer simulations indicate that the algori...

We investigate the use of adaptive MCMC algorithms to automatically tune the Markov chain parameters during a run. Examples include the Adaptive Metropolis (AM) multivariate algorithm of Haario et al. (2001), Metropolis-within-Gibbs algorithms for non-conjugate hierarchical models, regionally adjusted Metropolis algorithms, and logarithmic scalings. Computer simulations indicate that the algori...