This paper presents a new mutation strategy for the Metropolis light transport algorithm, which works in the unit cube of pseudo-random numbers instead of mutating in the path space. This transformation makes the integrand have lower variation and thus increases the acceptance probability of the mutated samples. Higher acceptance ratio, in turn, reduces the correlation of the samples, which inc...

We study the in-network computation of arbitrary functions whose computation schema is a complete binary tree, i.e., we assume that the network wants to compute a function of K operands, each of which is available at a distinct node in the network, and rather than simply collecting the K operands at a single sink node and computing the function, we want to compute the function during the proces...

In this paper, we study the asymptotic efficiency of the delayed rejection strategy. In particular, the efficiency of the delayed rejection Metropolis-Hastings algorithm is compared to that of the regular Metropolis algorithm. To allow for a fair comparison, the study is carried under optimal mixing conditions for each of these algorithms. After introducing optimal scaling results for the delay...

In this paper we study the Metropolis algorithm in connection with two mean–field spin systems, the so called mean–field Ising model and the Blume–Emery–Griffiths model. In both this examples the naive choice of proposal chain gives rise, for some parameters, to a slowly mixing Metropolis chain, that is a chain whose spectral gap decreases exponentially fast (in the dimension N of the problem)....

The development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in hig...

However, because the modeling environment of Simulink is a mixed and heterogeneous one as the combinations of discrete semantics, continuous semantics, etc. Usually it is not very easy to understand a Simulink model. A better way to represent a Simulink model may be necessary. One possible method is to separate the model into sub-models which have relatively simpler, well-defined semantics. Wit...

We consider Metropolis Glauber dynamics for sampling proper qcolourings of the n-vertex complete b-ary tree when 3 ≤ q ≤ b/2 ln(b). We give both upper and lower bounds on the mixing time. For fixed q and b, our upper bound is nO(b/ log b) and our lower bound is nΩ(b/q log(b)), where the constants implicit in the O() and Ω() notation do not depend upon n, q or b.