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The weighted stochastic simulation algorithm (wSSA) recently introduced by Kuwahara and Mura [J. Chem. Phys. 129, 165101 (2008)] is an innovative variation on the stochastic simulation algorithm (SSA). It enables one to estimate, with much less computational effort than was previously thought possible using a Monte Carlo simulation procedure, the probability that a specified event will occur in a chemically reacting system within a specified time when that probability is very small. This paper presents some procedural extensions to the wSSA that enhance its effectiveness in practical applications. The paper also attempts to clarify some theoretical issues connected with the wSSA, including its connection to first passage time theory and its relation to the SSA. [ABSTRACT FROM AUTHOR]

This paper deals with Monte Carlo simulation in case of dependent input random variables. We propose an algorithm to generate refined descriptive samples from dependent random variables for estimation of expectations of functions of output variables using the Iman and Conover algorithm to transform the dependent variables to independent ones. Therefore, such estimates obtained through a chosen mathematical model are compared with those obtained using the simple random sampling method, which proved that the former are the most efficient. Besides, using already published work on independent input variables, we can deduce in case of dependent input random variables, that asymptotically the variance of the RDS estimator is less than that of SRS estimator for any simulation function having finite second moment. [ABSTRACT FROM AUTHOR]

This paper describes the use of a Monte Carlo model incorporating a finite-element method computing the electrostatic fields inside and outside a semiconductor, plus a ray-tracing algorithm for determining the doping contrast observed in a scanning electron microscope (SEM). This combined numerical method also enables the effects on the doping contrast of surface band-bending to be distinguished from those of external patch fields outside the specimen, as well as any applied macroscopic external fields from the detection system in the SEM. Good agreement of our new theory with experiment is obtained. The contrast characteristics in energy-filtered secondary electron images are also explained. The results of this work lead to a more advanced understanding of the doping contrast mechanisms, thereby enabling quantitative dopant profiling using the SEM. [ABSTRACT FROM AUTHOR]

In this paper a new algorithm is presented that improves the efficiency of Wang and Landau algorithm or density of states (DOS) Monte Carlo simulations by employing rejected states. The algorithm is shown to have a performance superior to that of the original Wang-Landau [F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001)] algorithm and the more recent configurational temperature DOS algorithm. The performance of the method is illustrated in the context of results for the Lennard-Jones fluid. [ABSTRACT FROM AUTHOR]

This paper formulates a hybrid Monte Carlo implementation of the Fourier path integral (FPI-HMC) approach with partial averaging. Such a hybrid Monte Carlo approach allows one to generate collective moves through configuration space using molecular dynamics while retaining the computational advantages associated with the Fourier path integral Monte Carlo method. In comparison with the earlier Metropolis Monte Carlo implementations of the FPI algorithm, the present HMC method is shown to be significantly more efficient for quantum Lennard-Jones solids and suggests that such algorithms may prove useful for efficient simulations of a range of atomic and molecular systems. [ABSTRACT FROM AUTHOR]

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]

Highlights • Uncertainty of the Unified Method for TEMs characterization is carried out. • The uncertainty is Evaluated by means of a Monte Carlo Simulation. • A sensitivity analysis, gives a comprehensive insight into the most critical issues. • Experimental results show that two proposed stimuli provide compatible estimates. Abstract In this paper the uncertainty evaluation of the recently proposed Unified Method for Thermo-Electric Module characterization is carried out. The measurement model is detailed and individual uncertainty contributions are highlighted, with close reference to the instrumentation and measurement setup. The uncertainty evaluation is performed by means of a Monte Carlo Simulation. The same algorithm is used to perform a sensitivity analysis, giving a comprehensive insight into the most critical issues of the proposed method and assessing the performance of the two adopted electrical stimuli. The experimental results thereby obtained are discussed and improvements to the measurement setup and technique are finally proposed. [ABSTRACT FROM AUTHOR]

Variable selection methods have been widely used for system identification. However, there is still a major challenge in producing parsimonious models with optimal model structures as popular variable selection methods often produce suboptimal model with redundant model terms. In the paper, stability orthogonal regression (SOR) is proposed to build a more compact model with fewer or no redundant model terms. The main idea of SOR is that multiple intermediate models are produced by orthogonal forward regression (OFR) using sub-sampling technique and then the final model is a combination of these intermediate model terms but does not include infrequently selected terms. The effectiveness of the proposed methods is analyzed in theory and also demonstrated using two numerical examples in comparison with some popular algorithms. [ABSTRACT FROM AUTHOR]

The ziggurat method is a fast random variable generation method introduced by Marsaglia and Tsang in a series of papers. We discuss how the ziggurat method can be implemented for low-discrepancy sequences, and present algorithms and numerical results when the method is used to generate samples from the normal and gamma distributions. [ABSTRACT FROM AUTHOR]

Surrogate models are often used to alleviate the computational burden for structural systems with expensively time-consuming simulations. In this paper, a new adaptive surrogate model based efficient reliability method is proposed to address the issues that many existing adaptive sequential sampling reliability methods are limited to the Kriging models and Krging model-based Monte Carlo simulation (MCS) reliability methods produce random results even without considering the uncertainty from initial samples. Three learning functions are developed for selecting the most suitable training sample points at each iteration, and the learning functions ψ σ and ψ m are generally suggested because they were found to perform a bit better in most of the cases. Furthermore, most of the newly selected training sample points are ensured to reside far away from existing sample points and reside as close to the limit-state functions as possible. Two stopping criterions are given to terminate the proposed adaptive sequential sampling algorithm. The main advantages of the proposed method are that it not only provides an efficient manner for structural reliability analysis with multiple failure modes to produce a determined result under without considering the uncertainty from initial samples, but also can be used, in principle, in any existing surrogate models. The accuracy and efficiency as well as applicability of the proposed method are demonstrated using three numerical examples. [ABSTRACT FROM AUTHOR]

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series. [ABSTRACT FROM AUTHOR]

Abstract: A novel Bayesian Monte Carlo method for monotonic models (BMCM) is described in this paper. The BMCM method enjoys the advantages of the recently developed method of Dynamic Bounds for the reliability assessment of monotonic models, and incorporates weighted logical dependence between neighboring points of the limit state equation (LSE) as prior information for improving computational speed. The BMCM benefits from the global uncertainty concept that links global and local uncertainties. This integrated approach is superior to other techniques used in safety assessment of monotonic models as it substantially increases the efficiency of Monte Carlo method. The formulation of BMCM is provided in this paper with an example showing its ability to dramatically improve efficiency of simulations. This is achieved by employing prior information obtained from monotonic models and outcomes of the preceding simulations. The theory and numerical algorithms of the BMCM method for multi-dimensional problems, and integration with a probabilistic finite element model, are discussed in this paper. These coupled models are applied to the 17th Street Flood Wall in New Orleans to assess reliability of a flood defence system. [Copyright &y& Elsevier]

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]

A number of mortgage prepayment models require a specification of the mortgage rate process. Usually, ad-hoc models are used (e.g., a Treasury yield plus some constant). Recently, a number of papers have appeared where the authors have utilized a mortgage rate implied by the current yield curve (the so-called endogenous mortgage rate). However, the existing computational algorithms suffer from the curse of dimensionality and, consequently, are problematic to use for full-scale problems. A computational algorithm, proposed in this paper, is tractable in the sense that its complexity is equivalent to the problem of mortgage valuation. Moreover, the algorithm does not require iterations. The numerical example is based on a PDE computation. An implementation of a Monte Carlo method is also discussed. [ABSTRACT FROM AUTHOR]

• Derived a relatively simple theory for the mean square deviation behavior of the fusion center for the diffusion LMS algorithm. • The fusion center behavior is a special case of the diffusion LMS algorithm where the fusion center and nodes only communicate with each other. • The theory is shown to be the same for both Combine-then-Adapt and Adapt-then-Combine diffusion strategies. • The theory can be used to predict the behavior of the fusion center weights for any collection of nodal weights, nodal step sizes, nodal frequencies and nodal noise powers. • The nodes help each other (alluded to in many of the referenced publications) is demonstrated by comparing the mean square deviation behavior of one node vs. ten nodes. This paper studies the stochastic behavior of a specific version of the Diffusion Least-Mean Square (DLMS) algorithm in a system identification framework for a cyclostationary white Gaussian input. The considered DLMS version has a fusion center. The input cyclostationary signal is modeled by a white random process with periodically time-varying power. The system parameters vary according to a random walk model. The paper focusses on the behavior of the fusion center for DLMS for the special case when the nodes communicate only with the fusion center and vice versa. Mathematical models are derived for the mean and mean-square-deviation (MSD) behavior of the fusion center adaptive weights as a function of the input cyclostationarity. It is shown that the behavior of the fusion center is the same for both Combine-Then-Adapt (CTA) and Adapt-Then-Combine (ATC) diffusion strategies. Monte Carlo simulations are shown in excellent agreement with the theory. Finally the model is used to study the design of the DLMS algorithm for different nodal step-sizes, cyclostationarities, noise powers and weighting coefficients. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for parsing natural images into middle level vision representations—regions, curves, and curve groups (parallel curves and trees). This algorithm is targeted for an integrated solution to image segmentation and curve grouping through Bayesian inference. The paper makes the following contributions. (1) It adopts a layered (or 2.1D-sketch) representation integrating both region and curve models which compete to explain an input image. The curve layer occludes the region layer and curves observe a partial order occlusion relation. (2) A Markov chain search scheme Metropolized Gibbs Samplers (MGS) is studied. It consists of several pairs of reversible jumps to traverse the complex solution space. An MGS proposes the next state within the jump scope of the current state according to a conditional probability like a Gibbs sampler and then accepts the proposal with a Metropolis-Hastings step. This paper discusses systematic design strategies of devising reversible jumps for a complex inference task. (3) The proposal probability ratios in jumps are factorized into ratios of discriminative probabilities. The latter are computed in a bottom-up process, and they drive the Markov chain dynamics in a data-driven Markov chain Monte Carlo framework. We demonstrate the performance of the algorithm in experiments with a number of natural images. [ABSTRACT FROM AUTHOR]

Purpose - This paper describes an approach for the automatic calculation of view factors between surfaces of arbitrary shape, when taking into account possible screening effects due to intermediate surfaces. Design/methodology/approach - The specifically developed numerical code is based on the utilization of boundary elements to fit the surfaces of an algorithm solving the shadow effect and on a Monte Carlo method for the numerical integrations. Findings - The code has been tested for a set of geometrical configurations. It was clearly shown that it obtains good results in terms of accuracy and computing time. Its accuracy increases when the mesh of radiative surfaces is finer. Research limitations/implications - The use of the code is limited to opaque surfaces separated by an isothermal semi-transparent medium which can be absorbent but not diffusing of the thermal radiation. Practical implications - The study of the radiative exchanges between opaque surfaces with shadow effects due to intermediate surfaces may have concrete practical applications by using this code. Indeed, the code has been used for an industrial application, in order to evaluate view factors inside an enclosure, in the framework of studies concerned with the thermal comfort inside cars. Originality/value - The originality of this paper lies in taking into account the surfaces of complex geometries by using a boundary elements approximation, the algorithm solving the shadow effect, based on the convexity of the quadrilateral in 2D or the polyhedron in 3D. [ABSTRACT FROM AUTHOR]

This paper addresses the task of locating and identifying an unknown number of objects of different types in an image. Baddeley & Van Lieshout (1993) advocate marked point processes as object priors, whereas Grenander & Miller (1994) use deformable template models. In this paper elements of both approaches are combined to handle scenes containing variable numbers of objects of different types, using reversible jump Markov chain Monte Carlo methods for inference (Green, 1995). The naive application of these methods here leads to slow mixing and we adapt the model and algorithm in tandem in proposing three strategies to deal with this. The first two expand the model space by introducing an additional 'unknown' object type and the idea of a variable resolution template. The third strategy, utilising the first two, augments the algorithm with classes of updates which provide intuitive transitions between realisations containing different numbers of cells by splitting or merging nearby objects. [ABSTRACT FROM PUBLISHER]

FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective. [ABSTRACT FROM PUBLISHER]

Abstract: This paper is concerned with the development of efficient algorithms for propagating parametric uncertainty within the context of the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) approach to the analysis of complex vibro-acoustic systems. This approach models the system as a combination of SEA subsystems and FE components; it is assumed that the FE components have fully deterministic properties, while the SEA subsystems have a high degree of randomness. The method has been recently generalised by allowing the FE components to possess parametric uncertainty, leading to two ensembles of uncertainty: a non-parametric one (SEA subsystems) and a parametric one (FE components). The SEA subsystems ensemble is dealt with analytically, while the effect of the additional FE components ensemble can be dealt with by Monte Carlo Simulations. However, this approach can be computationally intensive when applied to complex engineering systems having many uncertain parameters. Two different strategies are proposed: (i) the combination of the hybrid FE/SEA method with the First Order Reliability Method which allows the probability of the non-parametric ensemble average of a response variable exceeding a barrier to be calculated and (ii) the combination of the hybrid FE/SEA method with Laplace's method which allows the evaluation of the probability of a response variable exceeding a limit value. The proposed approaches are illustrated using two built-up plate systems with uncertain properties and the results are validated against direct integration, Monte Carlo simulations of the FE and of the hybrid FE/SEA models. [Copyright &y& Elsevier]

In this paper a model of the classification task, based on the Monte Carlo theory of stochastic algorithms, is presented. The model allows different classification paradigms to be described, such as the single classifier, the ensemble, and the crowdsourcing ones. Some experimental work validates the theoretical predictions about the relatioship between the mean and variance of the classification accuracy and the number of classifiers. [ABSTRACT FROM AUTHOR]

This paper defines a new procedure for optimising wind farm turbine placement by means of Monte Carlo simulation method. To verify the algorithm's accuracy, an experimental wind farmwas tested in a wind tunnel. On the basis of experimentalmeasurements, the error on wind farmpower output was less than 4%.Theoptimizationmaximises the energy production criterion; wind turbines' ground positionswere used as independent variables. Moreover, the mathematical model takes into account annual wind intensities and directions and wind turbine interaction. The optimization of a wind farm on a real site was carried out using measured wind data, dominant wind direction, and intensity data as inputs to run the Monte Carlo simulations. There were 30 turbines in the wind park, each rated at 20kW. This choice was based on wind farmeconomics. The site was proportionally divided into 100 square cells, taking into account a minimum windward and crosswind distance between the turbines. The results highlight that the dominant wind intensity factor tends to overestimate the annual energy production by about 8%.Thus, the proposed method leads to a more precise annual energy evaluation and to a more optimal placement of the wind turbines. [ABSTRACT FROM AUTHOR]

In large-scale networks, the structure of the underlying network changes frequently, and thus the power iteration method for Personalized PageRank computation cannot deal with this kind of dynamic network efficiently In this paper, we design a Monte Carlo-based incremental method for Personalized PageRank computation. In a dynamic network, first, we do a random walk starting from each node and save the performed walks into a fingerprint database; second, we update the fingerprint database in a fixed time interval with our proposed update algorithm; finally, when a query is issued by a user, we estimate the Personalized PageRank vector by our proposed approximation algorithm. Experiments on real-world networks show that our method can handle multichanges of the underlying network at a time and is more efficient than related work, so it can be used in real incremental Personalized PageRank-based applications. [ABSTRACT FROM AUTHOR]

Abstract: The paper is devoted to the computational time-domain formulation of linear viscoelastic systems submitted to a nonstationary stochastic excitation and in the presence of model uncertainties which are modeled in the framework of the probability theory. The objective is to introduce and to develop an adapted and complete formulation of such a problem in the context of computational mechanics. A reduced-order model in the time domain with stochastic excitation is derived from the computational model. For the reduced-order model, the stochastic modeling of both computational model-parameter uncertainties and modeling errors is carried out using the nonparametric probabilistic approach and the random matrix theory. We present a new formulation of model uncertainties to construct the random operators for viscoelastic media. We then obtained a linear Stochastic Integro-Differential Equation (SIDE) with random operators and with a stochastic nonhomogeneous part (stochastic excitation). A time discretization of this SIDE is proposed. In a first step, the SIDE is transformed to a linear Itô Stochastic Differential Equation (ISDE) with random operators. Then the ISDE is discretized using an extension of the Störmer–Verlet scheme which is a particularly well adapted algorithm for long-time good behavior of the numerical solution. Finally, for the stochastic solver and statistical estimations of the random responses, we propose to use the Monte Carlo simulation for Gaussian and non-Gaussian excitations. [Copyright &y& Elsevier]

Abstract: In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. Although various models have been proposed in the literature, they often require the use of very large collections of data in order to fit them and display great sensitivity to changes in the radio propagation environment. In this work we advocate the use of switching multiple models that account for different classes of target dynamics and propagation environments and propose a flexible probabilistic switching scheme. The resulting state-space structure is termed a generalized switching multiple model (GSMM) system. Within this framework, we investigate two types of models for the RSS data: polynomial models and classical logarithmic path-loss representation. The first model is more accurate however it demands an offline model fitting step. The second one is less precise but it can be fitted in an online procedure. We have designed two tracking algorithms built around a Rao-Blackwellized particle filter, tailored to the GSMM structure and assessed its performances both with synthetic and experimental measurements. [Copyright &y& Elsevier]

Abstract: Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms. [Copyright &y& Elsevier]

Cardiac repolarization alternans is an electrophysiologic condition identified by a beat-to-beat fluctuation in action potential waveform. It has been mechanistically linked to instances of T-wave alternans, a clinically defined ECG alternation in T-wave morphology, and associated with the onset of cardiac reentry and sudden cardiac death. Many alternans detection algorithms have been proposed in the past, but the majority have been designed specifically for use with T-wave alternans. Action potential duration (APD) signals obtained from experiments (especially those derived from optical mapping) possess unique characteristics, which requires the development and use of a more appropriate alternans detection method. In this paper, we present a new class of algorithms, based on the Monte Carlo method, for the detection and quantitative measurement of alternans. Specifically, we derive a set of algorithms (one an analytical and more efficient version of the other) and compare its performance with the standard spectral method and the generalized likelihood ratio test algorithm using synthetic APD sequences and optical mapping data obtained from an alternans control experiment. We demonstrate the benefits of the new algorithm in the presence of Gaussian and Laplacian noise and frame-shift errors. The proposed algorithms are well suited for experimental applications, and furthermore, have low complexity and are implementable using fixed-point arithmetic, enabling potential use with implantable cardiac devices. [ABSTRACT FROM PUBLISHER]

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]

Abstract: This paper considers the problem of finite element model (FEM) updating in the context of model selection. The FEM updating problem arises from the need to update the initial FE model that does not match the measured real system outputs. This inverse system identification-problem is made even more complex by the uncertainties in modeling some of the structural parameters. Such uncertainty often results in a number of competing forms of FE models being proposed which leads to lack of consensus in the field. A model can be formulated in a number of ways; by the number, the location and the form of the updating parameters. We propose the use of a Bayesian evidence statistic to help decide on the best model from any given set of models. This statistic uses the recently developed stochastic nested sampling algorithm whose by-product is the posterior samples of the updated model parameters. Two examples of real structures are each modeled by a number of competing finite element models. The individual model evidences are compared using the Bayes factor, which is the ratio of evidences. Jeffrey''s scale is then used to determine the significance of the model differences obtained through the Bayes factor. [Copyright &y& Elsevier]

Abstract: This paper develops a mathematical model to quantify the market share of a given port. A Monte Carlo simulation based algorithm is proposed to estimate the port market share. A study is subsequently conducted to explore the characteristics of the landbridge system in China and Southeast Asia, and the impact of the landbridge system on the market shares of Asian ports is evaluated using the proposed approach for three scenarios. The competitiveness of the Malacca and Singapore Straits is also illustrated by analyzing the changes in the market share of Singapore port with respect to value of time. [Copyright &y& Elsevier]

Time-varying GARCH-Mmodels are commonly used in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets. [ABSTRACT FROM AUTHOR]

Matrix balancing may effect the stability of algorithms in matrix computations and the accuracy of computed solutions. In this paper, we first introduce an algorithm for matrix balancing. Then, using Monte Carlo method we propose a robust algorithm to evaluate dominant eigenpair of a given matrix. Finally, several randomly generated examples are presented to show the efficiency of the new method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data. [ABSTRACT FROM AUTHOR]

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]

Abstract: This paper presents a spreadsheet computational framework for implementing an advanced Monte Carlo method called Subset Simulation for uncertainty propagation that can provide better resolution for low failure probability level at the same time retaining some robustness features of direct Monte Carlo. While the efficiency of Subset Simulation has been demonstrated by numerous studies, attention in this work is devoted to application robustness of the spreadsheet framework. This concern is relevant because advanced Monte Carlo algorithms, or in general variance reduction techniques, gain their efficiency by exploiting information about the problem, which may require intrusive exchange of information with the system analysis model during the simulation process. To explore and authenticate implementation issues, a prototype Visual Basic Application (VBA) package is developed that can perform efficient uncertainty propagation by plugging as an Add-In into a spreadsheet that performs deterministic analysis. The resulting uncertainty propagation process is non-intrusive, requiring immaterial modification of the deterministic analysis spreadsheet. Operationally the proposed framework divides the whole process into system modeling (deterministic analysis), uncertainty modeling (generation of random variables) and uncertainty propagation (Subset Simulation). It is hoped that the development work can promote the use of advanced Monte Carlo simulation tools for uncertainty propagation in the decision-making process. [Copyright &y& Elsevier]

Multistate systems (MSS) are systems in which the system and its components are characterized by multiple states or performance levels. Component importance or sensitivity analysis facilitates the identification of vulnerabilities within the system, and aids in the quantification of criticalities of the system components. Multistate component importance analysis poses unique challenges to existing methods that are primarily based on binary-state applications. This paper presents an analytical method based on multistate multivalued decision diagrams (MMDD) for multistate component importance analysis. The contribution of this work is two-fold: 1) a novel, efficient algorithm for directly generating an MMDD model from multistate capacity network specifications without inefficient enumeration of multistate minimal path or cut vectors; and 2) an efficient, exact MMDD-based approach for evaluating MSS reliability and importance measures. The advantages of the proposed method are illustrated through a comparison with existing methods, and through detailed analyses of three case studies. [ABSTRACT FROM AUTHOR]

In order to design the automotive components considering probabilistic uncertainties in the decision variables, it is desired to perform a robust design process. The peak crushing force of the energy absorber components is one of the important objectives of the design of such components. In this paper, at first, the peak crushing force of the S-shaped box beams, as a highly simplified model of front member of a vehicle body, is extracted mathematically. Using such obtained mathematical model and Monte Carlo simulation, genetic algorithm is then used for the robust design of the S-shaped box beams having probabilistic uncertainties in material and geometrical parameters. In this way the variance-per-mean ratio of the peak crushing force (Pmax) is considered as the objective function. It is shown that some interesting relationships as useful robust design principles involved in the performance of the S-shaped box beams can be discovered by the robust design of the obtained mathematical model. [ABSTRACT FROM AUTHOR]

Abstract: The Particle Flow (PFlow) approach to calorimetry promises to deliver unprecedented jet energy resolution for experiments at future high energy colliders such as the proposed International Linear Collider (ILC). This paper describes the PandoraPFA particle flow algorithm which is then used to perform the first systematic study of the potential of high granularity PFlow calorimetry. For simulated events in the ILD detector concept, a jet energy resolution of is achieved for 40–400GeV jets. This result, which demonstrates that high granularity PFlow calorimetry can meet the challenging ILC jet energy resolution goals, does not depend strongly on the details of the Monte Carlo modelling of hadronic showers. The PandoraPFA algorithm is also used to investigate the general features of a collider detector optimised for high granularity PFlow calorimetry. Finally, a first study of the potential of high granularity PFlow calorimetry at a multi-TeV lepton collider, such as CLIC, is presented. [Copyright &y& Elsevier]

This paper considers the performance, on the binary erasure channel, of low-density parity-check (LDPC) codes without repeated edges in their Tanner graphs. A modification to existing finite-length analysis algorithms is presented for these codes. [ABSTRACT FROM AUTHOR]

The Mumford-Shah model is one of the most successful image segmentation models. However, existing algorithms for the model are often very sensitive to the choice of the initial guess. To make use of the model effectively, it is essential to develop an algorithm which can compute a global or near global optimal solution efficiently. While gradient descent based methods are well-known to find a local minimum only, even many stochastic methods do not provide a practical solution to this problem either. In this paper, we consider the computation of a global minimum of the multiphase piecewise constant Mumford-Shah model. We propose a hybrid approach which combines gradient based and stochastic optimization methods to resolve the problem of sensitivity to the initial guess. At the heart of our algorithm is a well-designed basin hopping scheme which uses global updates to escape from local traps in a way that is much more effective than standard stochastic methods. In our experiments, a very high-quality solution is obtained within a few stochastic hops whereas the solutions obtained with simulated annealing are incomparable even after thousands of steps. We also propose a multiresolution approach to reduce the computational cost and enhance the search for a global minimum. Furthermore, we derived a simple but useful theoretical result relating solutions at different spatial resolutions. [ABSTRACT FROM AUTHOR]

Abstract: The solution of a number of environmental models is incorrectly obtained by linearizing a nonlinear analytical solution. The linearization can yield a model that includes a common variable on both sides of the equal sign (i.e., ratio analysis), which in calibration causes highly inflated goodness-of-fit statistics. These specious practices continue likely because of tradition, i.e., “that is the way it is done”. Goodness-of-fit statistics that result from these erroneous practices do not accurately reflect the actual prediction accuracy of the model. Additionally, the linearly calibrated coefficients can be poor estimators of the true coefficients. The goal of this paper is to demonstrate the pitfalls of models based on ratio analyses. Several environmental models are used to demonstrate the erroneous procedure. Monte Carlo simulation is used to show the distribution of the true correlation coefficient and compare it to the distribution that results from the erroneous linearization. Linearization can produce correlation coefficients above 0.9 when the actual correlation is near 0. Nonlinear least squares algorithms can be used to more accurately fit nonlinear data to nonlinear models. [Copyright &y& Elsevier]

Abstract: Quasi-Monte Carlo (QMC) methods have been successfully used to compute high-dimensional integrals arising in many applications, especially in finance. To understand the success and the potential limitation of QMC, this paper focuses on quality measures of point sets in high dimensions. We introduce the order-, superposition and truncation discrepancies, which measure the quality of selected projections of a point set on lower-dimensional spaces. These measures are more informative than the classical ones. We study their relationships with the integration errors and study the tractability issues. We present efficient algorithms to compute these discrepancies and perform computational investigations to compare the performance of the Sobol’ nets with that of the sets of Latin hypercube sampling and random points. Numerical results show that in high dimensions the superiority of the Sobol’ nets mainly derives from the one-dimensional projections and the projections associated with the earlier dimensions; for order-2 and higher-order projections all these point sets have similar behavior (on the average). In weighted cases with fast decaying weights, the Sobol’ nets have a better performance than the other two point sets. The investigation enables us to better understand the properties of QMC and throws new light on when and why QMC can have a better (or no better) performance than Monte Carlo for multivariate integration in high dimensions. [Copyright &y& Elsevier]

Path integral algorithms are developed for evaluating European-style Asian options within three new families of multi-parameter local volatility models. The forward price process is a martingale under an assumed risk-neutral measure and the transition probability densities for such models are given in analytically closed form. Implied volatility surfaces for these models exhibit a wide range of pronounced smiles and skews of the type observed in the option markets. By concatenating the pricing kernels in time, we obtain an exact multidimensional path integral representation for any discrete-time path-dependent option on these models. Nonlinear variable transformations and numeraire changes are exploited in simplifying the path integrals. Efficient weighted Monte Carlo schemes for accurate evaluation of the integrals are then presented. The algorithms combine variance reduction techniques such as the control variate method with distributed computing to significantly increase efficiency. Our numerical results show that distributed Monte Carlo simulations scale almost linearly with the increase in independent processors on a high-performance computer cluster. As a competitive approach we develop a multinomial lattice method that is based on the backward propagation of the option value function using an updating rule. The paper includes simulation results for two kinds of Asian options: average price and average strike options. [ABSTRACT FROM AUTHOR]

This paper presents new techniques coupling uncertainty propagation with the transmission-line matrix method. This improves the quality of electromagnetic simulation. The reason is that in this case the solution provides not only the average field value but also the associated covariances. This is very useful when incomplete or uncertain information is inherent to the problem. The technique is much faster than regular Monte Carlo simulations, which are used in other methods. Copyright © 2007 John Wiley & Sons, Ltd. [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 a self-compensating pulse code modulation compound flow control valve and its self-compensating algorithm are introduced. After adopting a self-compensating method, the compound valve can maintain good control quality when one or more turn on/off valves (TOVs) fail by adjusting the activities of unfailed TOVs. A stochastic fault model for the compound valve is established and a Monte Carlo approach is used to calculate its life distribution. The results indicate that there is about 20–50% increase of mean controllable life. This might be of great importance when immediate emergency shutdown is not allowable or too costly, such as in the case of aircraft control and in the control of continuous processes. The extra life can leave a large enough time margin to plan a more graceful shutdown and maintenance. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]