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The aim of this paper is to propose a localization algorithm in which nodes are able to estimate their speeds, directions and motion types. By this way, node's next state can be estimated and the particles can be distributed closer to the predicted locations. Hence, accuracy and the precision of the localization are increased considerably. Sequential Monte Carlo method is used to represent the posterior distribution of a node's possible locations with a set of weighted samples. The developed algorithm is designed for a general network where no restrictions are made on the densities and the distributions of the nodes. Eight localization algorithms, namely Centroid, APIT, Amorphous, DV-Hop, MCL, SMCL, MCB and the developed algorithm SMCLA are implemented and compared for different parameters and mobility models. Simulation results show that the developed algorithm performs best for each of the simulation comparisons. [ABSTRACT FROM AUTHOR]

In this paper, the context of modeling of the impact of mismatch and statistical variations on analogue circuit building blocks is emphasized. The aim is to develop a new algorithm which predicts the statistical behavior of important parameters of an amplifier including output resistance, voltage gain and trans-conductance. The relative error of standard deviation of statistical parameters will remain less than 5% compared with the most accurate Monte-Carlo (MC) simulations using atomistic library model-cards. In comparison with other models which are based on the normal distribution of parameters, the proposed model does not need this limiting presumption. On the other hand, the proposed algorithm is more efficient compared with time consuming MC atomistic simulations. [ABSTRACT FROM AUTHOR]

The microscopic spatial kinetic Monte Carlo (KMC) method has been employed extensively in materials modeling. In this review paper, we focus on different traditional and multiscale KMC algorithms, challenges associated with their implementation, and methods developed to overcome these challenges. In the first part of the paper, we compare the implementation and computational cost of the null-event and rejection-free microscopic KMC algorithms. A firmer and more general foundation of the null-event KMC algorithm is presented. Statistical equivalence between the null-event and rejection-free KMC algorithms is also demonstrated. Implementation and efficiency of various search and update algorithms, which are at the heart of all spatial KMC simulations, are outlined and compared via numerical examples. In the second half of the paper, we review various spatial and temporal multiscale KMC methods, namely, the coarse-grained Monte Carlo (CGMC), the stochastic singular perturbation approximation, and the τ-leap methods, introduced recently to overcome the disparity of length and time scales and the one-at-a time execution of events. The concepts of the CGMC and the τ-leap methods, stochastic closures, multigrid methods, error associated with coarse-graining, a posteriori error estimates for generating spatially adaptive coarse-grained lattices, and computational speed-up upon coarse-graining are illustrated through simple examples from crystal growth, defect dynamics, adsorption–desorption, surface diffusion, and phase transitions. [ABSTRACT FROM AUTHOR]

If the three dimensional self-avoiding walk (SAW) is conformally invariant, then one can compute the hitting densities for the SAW in a half-space and in a sphere (Kennedy in Phys Rev Lett 111: 165703, ). The ensembles of SAW's used to define these hitting densities involve walks of arbitrary lengths, and so these ensembles cannot be directly studied by the pivot Monte Carlo algorithm for the SAW. We show that these mixed length ensembles should have the same scaling limit as certain weighted ensembles that only involve walks with a single length, thus providing a fast method for simulating these ensembles. Preliminary simulations which found good agreement between the predictions and Monte Carlo simulations for the SAW were reported in Kennedy (Phys Rev Lett 111: 165703, ). In this paper we present more accurate simulations testing the predictions and find even stronger support for the prediction that the SAW is conformally invariant in three dimensions. [ABSTRACT FROM AUTHOR]

A methodology is proposed that is suitable for efficient simulation of continuous-time Markov chains that are nearly-completely decomposable. For such Markov chains the effort to adequately explore the state space via Crude Monte Carlo (CMC) simulation can be extremely large. The purpose of this paper is to provide a fast alternative to the standard CMC algorithm, which we call Aggregate Monte Carlo (AMC). The idea of the AMC algorithm is to reduce the jumping back and forth of the Markov chain in small subregions of the state space. We accomplish this by aggregating such problem regions into single states. We discuss two methods to identify collections of states where the Markov chain may become 'trapped': the stochastic watershed segmentation from image analysis, and a graph-theoretic decomposition method. As a motivating application, we consider the problem of estimating the charge carrier mobility of disordered organic semiconductors, which contain low-energy regions in which the charge carrier can quickly become stuck. It is shown that the AMC estimator for the charge carrier mobility reduces computational costs by several orders of magnitude compared to the CMC estimator. [ABSTRACT FROM AUTHOR]

One of the critical limits on the sensitivity of direct neutrino mass experiment, such as MARE, is the background generated by unidentified double pulses. In our work developing an analysis system for MARE we have focused on comparing algorithms, and developing and testing an optimum algorithm for the identification (and removal) of double pulses. To test the algorithms we developed a Monte Carlo simulation that, based on the detectors physical parameters, generates realistic single and double events with known characteristics. In this paper we present our preliminary results on two algorithms used for double pulse identification and their efficiency as a function of pulse heights and separation. [ABSTRACT FROM AUTHOR]

In this paper, three new algorithms are introduced in order to explore long memory in financial time series. They are based on a new concept of fractal dimension of a curve. A mathematical support is provided for each algorithm and its accuracy is tested for different length time series by Monte Carlo simulations. In particular, in the case of short length series, the introduced algorithms perform much better than the classical methods. Finally, an empirical application for some stock market indexes as well as some individual stocks is presented. [ABSTRACT FROM AUTHOR]

Let X be a random vector with distribution μ on ℝ and Φ be a mapping from ℝ to ℝ. That mapping acts as a black box, e.g., the result from some computer experiments for which no analytical expression is available. This paper presents an efficient algorithm to estimate a tail probability given a quantile or a quantile given a tail probability. The algorithm improves upon existing multilevel splitting methods and can be analyzed using Poisson process tools that lead to exact description of the distribution of the estimated probabilities and quantiles. The performance of the algorithm is demonstrated in a problem related to digital watermarking. [ABSTRACT FROM AUTHOR]

We consider random geometric models for telecommunication access networks and analyse their serving zones which can be given, for example, by a class of so-called Cox–Voronoi tessellations (CVTs). Such CVTs are constructed with respect to locations of network components, the nucleii of their induced cells, which are scattered randomly along lines induced by a Poisson line process. In particular, we consider two levels of network components and investigate these hierarchical models with respect to mean shortest path length and mean subscriber line length, respectively. We explain point-process techniques which allow for these characteristics to be computed without simulating the locations of lower-level components. We sustain our results by numerical examples which were obtained through Monte Carlo simulations, where we used simulation algorithms for typical Cox–Voronoi cells derived in a previous paper. Also, briefly, we discuss tests of correctness of the implemented algorithms. Finally, we present a short outlook to possible extensions concerning multi-level models and iterated random tessellations. [ABSTRACT FROM AUTHOR]

This paper describes a new approach based on ant colony optimization (ACO) metaheuristic and Monte Carlo (MC) simulation technique, for project crashing problem (PCP) under uncertainties. To our knowledge, this is the first application of ACO technique for the stochastic project crashing problem (SPCP), in the published literature. A confidence-level-based approach has been proposed for SPCP in program evaluation and review technique (PERT) type networks, where activities are subjected to discrete cost functions and assumed to be exponentially distributed. The objective of the proposed model is to optimally improve the project completion probability in a prespecified due date based on a predefined probability. In order to solve the constructed model, we apply the ACO algorithm and path criticality index, together. The proposed approach applies the path criticality concept in order to select the most critical path by using MC simulation technique. Then, the developed ACO is used to solve a nonlinear integer mathematical programming for selected path. In order to demonstrate the model effectiveness, a large scale illustrative example has been presented and several computational experiments are conducted to determine the appropriate levels of ACO parameters, which lead to the accurate results with reasonable computational time. Finally, a comparative study has been conducted to validate the ACO approach, using several randomly generated problems. [ABSTRACT FROM AUTHOR]

Kinetic Monte Carlo methods provide a powerful computational tool for the simulation of microscopic processes such as the diffusion of interacting particles on a surface, at a detailed atomistic level. However such algorithms are typically computationally expensive and are restricted to fairly small spatiotemporal scales. One approach towards overcoming this problem was the development of coarse-grained Monte Carlo algorithms. In recent literature, these methods were shown to be capable of efficiently describing much larger length scales while still incorporating information on microscopic interactions and fluctuations. In this paper, a coarse-grained Langevin system of stochastic differential equations as approximations of diffusion of interacting particles is derived, based on these earlier coarse-grained models. The authors demonstrate the asymptotic equivalence of transient and long time behavior of the Langevin approximation and the underlying microscopic process, using asymptotics methods such as large deviations for interacting particles systems, and furthermore, present corresponding numerical simulations, comparing statistical quantities like mean paths, auto correlations and power spectra of the microscopic and the approximating Langevin processes. Finally, it is shown that the Langevin approximations presented here are much more computationally efficient than conventional Kinetic Monte Carlo methods, since in addition to the reduction in the number of spatial degrees of freedom in coarse-grained Monte Carlo methods, the Langevin system of stochastic differential equations allows for multiple particle moves in a single timestep. [ABSTRACT FROM AUTHOR]

In this paper work we present a phase-field/Monte-Carlo hybrid algorithm for the simulation of solutal growth of organic crystals. The algorithm is subsequently used for an investigation of diffusion effects on the growth mechanisms. This method combines a two-scale phase-field model of the liquid phase epitaxial growth and a Monte-Carlo algorithm of the 2D nucleation and thus is faster than previous purely Monte Carlo simulations of crystal growth. The inclusion of supersaturation and diffusion in the method allows the study of crystal growth under various growth conditions. Parameters used in the hybrid algorithm are bound to the energetic parameters of crystal faces, which can be estimated from a detailed study of the actual crystal structure based on a connected nets analysis, which allows the prediction of the shape and morphology of real crystals. The study of the diffusion effect is carried out based on an example of a hydroquinone crystal, which grows from the water solution at various supersaturations. The dependencies of the growth rate and the nucleation rate on the supersaturation indicate the change of the growth mechanism from spiral growth to 2D nucleation. The difference in the growth rate for various faces is in agreement with the crystal morphologies derived from the attachment energy method and observed experimentally. The main result of the simulation is the evaluation of engineering limits for choosing appropriate external process conditions. [ABSTRACT FROM AUTHOR]

The paper is devoted to the development of Cox point processes driven by nonnegative processes of Ornstein–Uhlenbeck (OU) type. Starting with multivariate temporal processes we develop formula for the cross pair correlation function. Further filtering problem is studied by means of two different approaches, either with discretization in time or through the point process densities with respect to the Poisson process. The first approach is described mainly analytically while in the second case we obtain numerical solution by means of MCMC. The Metropolis–Hastings birth–death chain for filtering can be also used when estimating the parameters of the model. In the second part we try to develop spatial and spatio-temporal Cox point processes driven by a stationary OU process. The generating functional of the point process is derived which enables evaluation of basic characteristics. Finally a simulation algorithm is given and applied. [ABSTRACT FROM AUTHOR]

The article presents the authors' comment on the research entitled "Parametric and Nonparametric Population Methods: Their Comparative Performance in Analysing a Clinical Dataset and Two Monte Carlo Simulation Studies." They claim that the performance of the parametric iterative two-stage Bayesian method in the first Monte Carlo simulation study is poor. They also present the result of their study with the use of the same simulation method.

Conditional simulation is useful in connection with inference and prediction for a generalized linear mixed model. We consider random walk Metropolis and Langevin-Hastings algorithms for simulating the random effects given the observed data, when the joint distribution of the unobserved random effects is multivariate Gaussian. In particular we study the desirable property of geometric ergodicity, which ensures the validity of central limit theorems for Monte Carlo estimates. [ABSTRACT FROM AUTHOR]

This study develops a novel Monte Carlo simulation (MCS) approach to estimate system reliability for a stochastic production system with finite buffer storage. System reliability indicates the probability of all workstations providing sufficient capacities to satisfy a specified demand, as well as that all buffer stations are not running out of storage. First, buffer stations are modeled in a stochastic production network (SPN) model and their storage usage is analyzed based on the network-structured SPN. Second, an MCS is developed to generate the system state and to check the storage usage of buffer stations to determine whether the demand can be satisfied. After repeated simulations, the system reliability of the SPN can be estimated. Experimental results show that the proposed MCS approach is effective and efficient in estimating system reliability with reasonable quality for an SPN within a reasonable time. More importantly, system reliability will be overestimated with infinite buffer storage, and thus, it is worth studying finite buffer storage. [ABSTRACT FROM AUTHOR]

Population annealing is a hybrid of sequential and Markov chain Monte Carlo methods geared towards the efficient parallel simulation of systems with complex free-energy landscapes. Systems with first-order phase transitions are among the problems in computational physics that are difficult to tackle with standard methods such as local-update simulations in the canonical ensemble, for example with the Metropolis algorithm. It is hence interesting to see whether such transitions can be more easily studied using population annealing. We report here our preliminary observations from population annealing runs for the two-dimensional Potts model with q > 4, where it undergoes a first-order transition. [ABSTRACT FROM AUTHOR]

In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics is dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. Implicit approximations have been developed to improve numerical stability and provide efficient simulation algorithms for those systems. Here, we propose an efficient Multilevel Monte Carlo (MLMC) method in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This method uses split-step implicit tau-leap (SSI-TL) at levels where the explicit-TL method is not applicable due to numerical stability issues. We present numerical examples that illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

We report on large-scale Wang-Landau Monte Carlo simulations of the critical behavior of two spin models in two- (2d) and three-dimensions (3d), namely the 2d random-bond Ising model and the pure 3d Blume-Capel model at zero crystal-field coupling. The numerical data we obtain and the relevant finite-size scaling analysis provide clear answers regarding the universality aspects of both models. In particular, for the random-bond case of the 2d Ising model the theoretically predicted strong universality's hypothesis is verified, whereas for the second-order regime of the Blume-Capel model, the expected d = 3 Ising universality is verified. Our study is facilitated by the combined use of the Wang-Landau algorithm and the critical energy subspace scheme, indicating that the proposed scheme is able to provide accurate results on the critical behavior of complex spin systems. [ABSTRACT FROM AUTHOR]

Reservoir system reliability is the ability of reservoir to perform its required functions under stated conditions for a specified period of time. In classical method of reservoir system reliability analysis, the operation policy is used in a simple simulation model, considering the historical/synthetic inflow series and a number of physical bounds on a reservoir system. This type of reliability analysis assumes a reservoir system as fully failed or functioning, called binary state assumption. A number of researchers from various research backgrounds have shown that the binary state assumption in the traditional reliability theory is not extensively acceptable. Our approach to tackle the present problem space is to implement the algorithm of advance first order second moment (AFOSM) method. In this new method, the inflow and reservoir storage are considered as uncertain variables. The mean, variance and covariance of uncertain variables are determined using moment values of reservoir state variables. For this purpose, a stochastic optimization model developed based on the constraint state formulation is applied. The proposed model of reliability analysis is used to a real case study in Iran. As a result, monthly probabilities of water allocation were computed from AFOSM method, and the outputs were compared with those from Monte Carlo method. The comparison shows that the outputs from AFOSM method are similar to those from the Monte Carlo method. In term of practical use of this study, the proposed method is appropriate to determine the monthly probability of failure in water allocation without the aid of simulation. [ABSTRACT FROM AUTHOR]

We consider a general class of branching methods with killing for the estimation of rare events. The class includes a number of existing schemes, including RESTART and DPR (Direct Probability Redistribution). A method for the design and analysis is developed when the quantity of interest can be embedded in a sequence whose limit is determined by a large deviation principle. A notion of subsolution for the related calculus of variations problem is introduced, and two main results are proved. One is that the number of particles and the total work scales subexponentially in the large deviation parameter when the branching process is constructed according to a subsolution. The second is that the asymptotic performance of the schemes as measured by the variance of the estimate can be characterized in terms of the subsolution. Some examples are given to demonstrate the performance of the method. [ABSTRACT FROM AUTHOR]

In a disaster situation, functionality of an infrastructure network is critical for effective emergency response. We evaluate several probabilistic measures of connectivity and expected travel time/distance between critical origin-destination pairs to assess the functionality of a given transportation network in case of a disaster. The input data include the most likely disaster scenarios as well as the probability that each link of the network fails under each scenario. Unlike most studies that assume independent link failures, we model dependency among link failures and propose a novel dependency model that incorporates the impact of the disaster on the network and at the same time yields tractable cases for the computation of the probabilistic measures. We develop algorithms for the computation of the measures and utilize a Monte Carlo simulation algorithm for the intractable cases. We present a case study of the Istanbul highway system under earthquake risk, and compare different dependency structures computationally. [ABSTRACT FROM AUTHOR]

The curse of dimensionality limits the accuracy of the quasi-Monte Carlo (QMC) method in high-dimensional problems. Imai and Tan (Proceedings of the 2002 winter simulation conference, 2002; Monte Carlo and Quasi-Monte Carlo Methods 2002, pp 275-292, Springer, Berlin, 2004; J Comput Finance 10(2):129-155, 2007) have proposed a dimension reduction technique, named linear transformation (LT), aiming to improve the efficiency of the QMC method. We investigate this approach in detail and make it more convenient. We implement a faster QR decomposition that considerably reduces the computational burden. The efficacy of our algorithm is illustrated by considering two high-dimensional option pricing problems: Asian basket options in the Black-Scholes (BS) model and Asian options in the Cox-Ingersoll-Ross (CIR) model. We employ a QMC generator only for the components selected by the LT construction and use Latin hypercube sampling (LHS) for all the others. Finally, we compare our results to those obtained by different random number generators and standard algorithms; subsequently, we benchmark our computational times against those presented in Imai and Tan (J Comput Finance 10(2):129-155, 2007). [ABSTRACT FROM AUTHOR]