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This paper describes a new Monte Carlo method based on a novel stochastic potential switching algorithm. This algorithm enables the equilibrium properties of a system with potential V to be computed using a Monte Carlo simulation for a system with a possibly less complex stochastically altered potential V. By proper choices of the stochastic switching and transition probabilities, it is shown that detailed balance can be strictly maintained with respect to the original potential V. The validity of the method is illustrated with a simple one-dimensional example. The method is then generalized to multidimensional systems with any additive potential, providing a framework for the design of more efficient algorithms to simulate complex systems. A near-critical Lennard-Jones fluid with more than 20 000 particles is used to illustrate the method. The new algorithm produced a much smaller dynamic scaling exponent compared to the Metropolis method and improved sampling efficiency by over an order of magnitude. [ABSTRACT FROM AUTHOR]

Abstract: A general repairable k-out-of-n:G system with non-identical components, which is a common form of redundancy, is considered in this paper. The number of repairmen is assumed to be r (1≤r≤n−k+1) while components can have similar or different repair priorities. The objective of this work is to address the problem of efficient evaluation of the system''s availability in a way that steady state solutions can be obtained systematically in a reasonable computational time. This problem is modeled as a finite state-dependent non-homogeneous quasi-birth–death (QBD) process. An algorithm is introduced to systematically generate the system state vectors and transition rate matrix and then an iterative method based on the Block Gauss–Seidel method is employed to determine the steady state probabilities. These are the novel contributions made in this paper. An analog Monte Carlo simulation is presented to demonstrate the correctness and the efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: The probability hypothesis density (PHD) filter is a promising algorithm for multitarget tracking, which can be extended for jump Markov systems (JMS). Since the existing multiple model sequential Monte Carlo PHD (MM SMC-PHD) filter is not interacting, two extensions of the SMC-PHD filters are developed in this paper. The interacting multiple-model (IMM) SMC-PHD filter approximates the model conditional PHD of target states by particles, and performs the interaction by resampling without any a priori assumption of the noise. The IMM Rao-Blackwellized particle (RBP) PHD filter uses the idea of Rao-Blackwellized to further enhance the performance of target state estimation for JMS with mixed linear/nonlinear state space models. The simulation results show that the proposed algorithms have better performances than the existing MM SMC-PHD filter in terms of state filtering and target number estimation. [Copyright &y& Elsevier]

Abstract: In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes–Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms. In addition, we propose new probabilistic metrics inspired by the concepts of recall and precision. The proposed metrics also have clear probabilistic interpretations and can be applied to validate and compare other soft and overlapping clustering algorithms. The approach is exemplified by using the Reuters-21578 multi-labeled collection to validate LDA models, then using Monte Carlo simulations to show the convergence to the correct results. Additional statistical evidence is provided to better understand the relation of the metrics presented. [Copyright &y& Elsevier]

We consider statistical inference from incomplete sets of binary data. Our approach is based on the autologistic model, which is very flexible and well suited for medical applications. We propose a Bayesian approach, essentially using Monte Carlo techniques. The method developed in this paper is a special version of Gibbs sampler. We repeat intermittently the following two steps. First, missing values are generated from the predictive distribution. Second, unknown parametes are estimated from the completed data. The Monte Carlo method of computing maximum likelihood estimates due to Geyer and Thompson (J. R. Statist. Soc. B 54: 657-699, 1992) is modified to the Bayesian setting and missing data problems. We include results of some small scale simulation experiments. We artificially introduce missing values in a real data set and then use our algorithm to refill missings. The rate of correct imputations is quite satisfactory. [ABSTRACT FROM AUTHOR]

A novel procedure for estimating the relative importance of uncertain parameters of complex FE model is presented. The method is specifically directed toward problems involving high-dimensional input parameter spaces, as they are encountered during uncertainty analysis of large scale, refined FE models. In these cases one is commonly faced with thousands of uncertain parameters and traditional techniques, e.g. finite difference or direct differentiation methods become expensive. In contrast, the presented method quickly filters out the most influential variables. Hence, the main objective is not to compute the sensitivity but to identify those parameters whose random variations have the biggest influence on the response. This is achieved by generating a set of samples with direct Monte Carlo simulation, which are closely scattered around the point at which the relative importance measures are sought. From these samples, estimators of the relative importance are synthesized and the most important ones are refined with a method of choice. In this paper, the underlying theory as well as the resulting algorithm is presented. [ABSTRACT FROM AUTHOR]

In this paper we present and test the functionality of a parcelization algorithm, implemented in our spatially explicit, agent-based land-use-change model which we call the Multi Agent-based Behavioral Economic Landscape (MABEL) model. In order to test the best possible spatial configuration of the algorithm and its efficiency compared with historically observed land-use changes, we employed a Monte Carlo simulation approach with a series of replication experiments across time, and compared observed changes between 1970 and 1990, and across two different landscapes in Michigan, USA. We compare the simulated parcel shapes with historically observed land-use changes using the landscape-ecology metric program, FRAGSTATS. [ABSTRACT FROM AUTHOR]