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...

In this paper we define and study new directional Metropolis–Hastings algorithms that propose states in hyperplanes. Each iteration in directional Metropolis–Hastings algorithms consist of three steps. First a direction is sampled by an auxiliary variable. Then a potential new state is proposed in the subspace defined by this direction and the current state. Lastly, the potential new state is a...

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...

Seied Rabie Mahdavi1 Ph.D., Alireza Shirazi2 Ph.D., Ali Khodadadee3 BS.c., Mostafa Ghaffory4 MS.c. and Asghar Mesbahi5 Ph.D 1Dept. of Medical Physics, Faculty of Medicine, Iran University of Medical Sciences, Tehran 2Dept. of Medical Physics, Faculty of Medicine, Tehran University of Medical Sciences, Tehran 3Azad Research and Science University, Tehran 4Secondary Standard Dosimetry Laboratory ...

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For SU(2) lattice gauge theory with the fundamental-adjoint action an efficient heat-bath algorithm is not known so that one had to rely on Metropolis simulations supplemented by overrelaxation. Implementing a novel biased Metropolis-heat-bath algorithm for this model, we find improvement factors in the range 1.45 to 2.06 over conventionally optimized Metropolis simulations. If one optimizes fu...

The Metropolis process is an extremely general recipe for constructing a Markov chain which has any desired stationary distribution π on a finite set Ω. Moreover, this distribution can be specified just by a weight function w : Ω → R, so that π(x) = w(x) Z where Z is an unknown normalizing factor. The Metropolis process is named after one of its inventors [MR+53]. To specify the Metropolis proc...

The random walk Metropolis algorithm is a simple Markov chain Monte Carlo scheme which is frequently used in Bayesian statistical problems. We propose a guided walk Metropolis algorithm which suppresses some of the random walk behavior in the Markov chain. This alternative algorithm is no harder to implement than the random walk Metropolis algorithm, but empirical studies show that it performs ...