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This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation. [ABSTRACT FROM AUTHOR]

The arrangement of atoms in the microstructures of alloys plays a key role in defining their physical and functional properties. These microstructures are studied to detect deviations from the expected homogeneous distribution using frequency distribution tests and cluster detection algorithms on reconstructions obtained from atom probe microscopy. However, it is challenging to evaluate and validate the performance of these algorithms using the reconstructed data because of the experimental limitations of the atom probe data itself. Low detector efficiency and reconstructional artifacts may lead to loss of existing clusters, or introduce heterogeneities that do not reflect the original arrangement. Additionally, retrieving atom probe data is a time and cost-prohibitive process. These challenges call for the use of simulated datasets with known concentrations and distributions to evaluate algorithms that analyze distributions or detect clusters. In this paper, we extend the existing methodology to simulate the creation of solute clusters in very dilute solutions for any solution. We propose methods to create denser and wider clusters in any solution irrespective of the concentration. Three novel solute cluster simulation techniques incremental, hierarchical and gravitational clustering are presented and compared in this paper. The simulated datasets can be used to validate and compare the performance of frequency distribution or cluster discovery algorithms. We compare the cluster analysis results obtained from the single linkage algorithm and DBSCAN to highlight the utility of these datasets. [ABSTRACT FROM AUTHOR]

A numerical algorithm is developed and tested that implements the kinetic treatment of electromagnetic radiation propagating through plasmas whose properties have small scale fluctuations, which was developed in a companion paper. This method incorporates the effects of refraction, damping, mode structure, and other aspects of large-scale propagation of electromagnetic waves on the distribution function of quanta in position and wave vector, with small-scale effects of nonuniformities, including scattering and mode conversion approximated as causing drift and diffusion in wave vector. Numerical solution of the kinetic equation yields the distribution function of radiation quanta in space, time, and wave vector. Simulations verify the convergence, accuracy, and speed of the methods used to treat each term in the equation. The simulations also illustrate the main physical effects and place the results in a form that can be used in future applications. [ABSTRACT FROM AUTHOR]

The stochastic simulation algorithm was introduced by Gillespie and in a different form by Kurtz. There have been many attempts at accelerating the algorithm without deviating from the behavior of the simulated system. The crux of the explicit τ-leaping procedure is the use of Poisson random variables to approximate the number of occurrences of each type of reaction event during a carefully selected time period, τ. This method is acceptable providing the leap condition, that no propensity function changes 'significantly' during any time-step, is met. Using this method there is a possibility that species numbers can, artificially, become negative. Several recent papers have demonstrated methods that avoid this situation. One such method classifies, as critical, those reactions in danger of sending species populations negative. At most, one of these critical reactions is allowed to occur in the next time-step. We argue that the criticality of a reactant species and its dependent reaction channels should be related to the probability of the species number becoming negative. This way only reactions that, if fired, produce a high probability of driving a reactant population negative are labeled critical. The number of firings of more reaction channels can be approximated using Poisson random variables thus speeding up the simulation while maintaining the accuracy. In implementing this revised method of criticality selection we make use of the probability distribution from which the random variable describing the change in species number is drawn. We give several numerical examples to demonstrate the effectiveness of our new method. [ABSTRACT FROM AUTHOR]

The Type-II progressive censoring scheme has become very popular for analyzing lifetime data in reliability and survival analysis. However, no published papers address parameter estimation under progressive Type-II censoring for the mixed exponential distribution (MED), which is an important model for reliability and survival analysis. This is the problem that we address in this paper. It is noted that maximum likelihood estimation of unknown parameters cannot be obtained in closed form due to the complicated log-likelihood function. We solve this problem by using the EM algorithm. Finally, we obtain closed form estimates of the model. The proposed methods are illustrated by both some simulations and a case analysis. [ABSTRACT FROM PUBLISHER]

SUMMARY Gang scheduling is an efficient resource management scheme for distributed systems which combines elements of time sharing and space sharing. It is a suitable technique particularly in the case when parallel tasks have to be running concurrently to make progress in communication. This paper studies the impact on scheduling performance when dynamically generated sequential gangs exist in the workload. In the case of sequential gangs, a subsequent gang can be dynamically generated after the execution of the initial gang based on affinity information which resides on the caches of the previously seized processors. The performance of different gang-scheduling algorithms is examined for various cases of workload compositions which range from cases with a low demand for dynamically generated gangs to cases with a high ratio of sequential gangs to solitary gangs. A simulation model is implemented to address associated performance issues. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Scale mixtures of normal distributions form a class of symmetric thick-tailed distributions that includes the normal one as a special case. In this paper we consider local influence analysis for measurement error models (MEM) when the random error and the unobserved value of the covariates jointly follow scale mixtures of normal distributions, providing an appealing robust alternative to the usual Gaussian process in measurement error models. In order to avoid difficulties in estimating the parameter of the mixing variable, we fixed it previously, as recommended by Lange et al. (J Am Stat Assoc 84:881-896, 1989) and Berkane et al. (Comput Stat Data Anal 18:255-267, 1994). The local influence method is used to assess the robustness aspects of the parameter estimates under some usual perturbation schemes. However, as the observed log-likelihood associated with this model involves some integrals, Cook's well-known approach may be hard to apply to obtain measures of local influence. Instead, we develop local influence measures following the approach of Zhu and Lee (J R Stat Soc Ser B 63:121-126, 2001), which is based on the EM algorithm. Results obtained from a real data set are reported, illustrating the usefulness of the proposed methodology, its relative simplicity, adaptability and practical usage. [ABSTRACT FROM AUTHOR]

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of the local search method (LS), which is based on the generalized reduced gradient algorithm, hybrid local search method is proposed by incorporating chaos. Thus, LS and chaos are hybridized to form a chaotic dynamic local search method (CDLS), which reasonably combines the searching ability of LS and chaotic searching behavior. In this paper, a CDLS method based on the logistic equation is presented to learn Multiple-Valued Logic (MVL) Networks. Simulation results and comparisons with the traditional back propagation algorithm (BP) and the standard LS method show that the CDLS can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of iterations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of multipath traffic engineering in optical burst switching (OBS) networks. The main goal of this work is to minimize burst loss rate in the network by adaptively balancing the burst traffic among multiple paths based on the measurement and analysis of path congestion. We develop three distributed traffic splitting algorithms by considering the special features of OBS networks. Among these three algorithms, one algorithm considers burst level traffic distribution, and the other two algorithms consider flow level traffic distribution to avoid packet reordering problem. The proposed multipath traffic engineering algorithms achieve significant performance improvement in reducing burst loss probability in a distributed manner in OBS networks. Further, through theoretical analysis and simulations, we show that the proposed algorithms converge quickly on a sudden traffic increase in the network. [ABSTRACT FROM AUTHOR]

A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous non-mixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous non-normal densities. This paper illustrates what happens when the EM algorithm for normal mixtures is applied to a distribution that is a homogeneous non-mixture distribution. In particular, a population-based EM algorithm for finite mixtures is introduced and applied directly to density functions instead of sample data. This algorithm is used to find finite mixture approximations to common homogeneous distributions. An example regarding the nature of a placebo response in drug treated depressed subjects is used to illustrate ideas. [ABSTRACT FROM AUTHOR]

This paper studies the probability distribution functions of the parameters to be learned and optimized in biped gait generation. By formulating the gait pattern generation into a multiobjective optimization problem with consideration of geometric and state constraints, dynamically stable and low energy cost biped gaits are generated and optimized by the proposed method, namely Spline-based Estimation of Distribution Algorithm (EDA) with Q-learning updating rule (EDA̱S̱Q). Instead of assuming variables as independent ones, the relationship between them is exploited by formulating the corresponding probability models with the Catmull-Rom cubic spline function. Such kind of function is proved to be a suboptimal and adaptive realization of the cubic spline function and is capable of providing high- precision description. Moreover, the probability models are up- dated autonomously by Q-learning method, which is model-free and adaptive. Thus, EDA̱S̱Q can deal with complex probability distribution functions without a prior knowledge about the distribution. The biped gait generated by EDA̱S̱Q has been verified using the simulation model of a humanoid soccer robot Robo-Erectus. It also shows that EDA̱S̱Q can generate the desired biped gaits autonomously in short learning epochs. An interpretation of the transition probability distribution achieved by EDA̱S̱Q provides us easy understanding for biped locomotion and better control in humanoid robots. [ABSTRACT FROM AUTHOR]

Purpose - Cox's model with Weibull distribution and Cox's with exponential distribution are the most important models in reliability analysis. This paper seeks to show that, with a large sample size based on expectation maximization (EM) algorithm, both models give similar results. Design/methodology/approach - The parameters of the models have been estimated by method of maximum likelihood based on EM algorithm. The objective of this analysis is to fit the modification of Cox's model with Weibull distribution and Cox's with exponential distribution, examine its performance and compare their results with Crowder et al. Findings - A simulation study indicates that the parametric Cox's model with Weibull distribution gives similar results to Cox's with exponential distribution, especially for a large sample size. Also, the modification of the two models showed better results compared with Crowder et al., especially for the second causes of failure. Originality/value - A modification of the two competing risk models has mostly been applied in failure time data and simulation data. The results of the simulation study indicate that the Weibull and exponential are suitable for Cox's model as they are easy to use and it can achieve even higher accuracy compared with other distribution models. [ABSTRACT FROM AUTHOR]

The use of options in altering the return distribution of stock portfolios has found increasing interest among investment managers. The potential of options in forming portfolio return distributions that are more consistent with investor preferences has been well developed, but determining the actual effect of options on portfolio returns remains a complex and unsolved problem. The purpose of this paper is to describe an algorithm for determining the return distribution that will result when option positions are taken on stocks in a portfolio. The distribution is determined for a finite time horizon, and does not rely on the assumption of continuous portfolio revisions. Simulation tests on the algorithm are also discussed. The algorithm is then used to illustrate the effect that several simple and well-known options strategies will have on portfolio return characteristics. This algorithm will be of practical value, giving a technique for generating and analyzing the expanded opportunity set that exists by using options in portfolio management. [ABSTRACT FROM AUTHOR]

This paper proposes a procedure of synthetic detection for the location of a change point and outliers in bilinear time series models with a change after an unknown time point. Based on Bayesian framework, we first derive the conditional posterior distribution of the change point and from that distribution estimate the position of the change point. Then we use these results to detect the outliers in the time series before and after that change point via Gibbs sampler algorithm. Our simulation studies show that the proposed procedure is effective. [ABSTRACT FROM PUBLISHER]

In order to carry out the simulation, we need a source of random numbers distributed according to the desired probability distribution. In this paper we have constructed algorithms for generating both continuous and discrete random variables. One simulates a discrete random variable having a geometric distribution, which is used in reliability.We also create some algorithms for generating: a normal continuous variable, other continuous variables having exponential distribution, Weibull distribution, gamma distribution. The aim cf this paper is to see that if we have random numbers generated according to some distribution, we may perform a transformation to generate the desired distribution. [ABSTRACT FROM AUTHOR]

In this paper we study the cure rate survival model involving a competitive risk structure with missing categorical covariates. A parametric distribution that can be written as a sequence of one-dimensional conditional distributions is specified for the missing covariates. We consider the missing data at random situation so that the missing covariates may depend only on the observed ones. Parameter estimates are obtained by using the EM algorithm via the method of weights. Extensive simulation studies are conducted and reported to compare estimates efficiency with and without missing data. As expected, the estimation approach taking into consideration the missing covariates presents much better efficiency in terms of mean square errors than the complete case situation. Effects of increasing cured fraction and censored observations are also reported. We demonstrate the proposed methodology with two real data sets. One involved the length of time to obtain a BS degree in Statistics, and another about the time to breast cancer recurrence. [ABSTRACT FROM AUTHOR]

The Stochastic Alpha Beta Rho Stochastic Volatility (SABR-SV) model is widely used in the financial industry for the pricing of fixed income instruments. In this paper we develop a low-bias simulation scheme for the SABR-SV model, which deals efficiently with (undesired) possible negative values in the asset price process, the martingale property of the discrete scheme and the discretization bias of commonly used Euler discretization schemes. The proposed algorithm is based the analytic properties of the governing distribution. Experiments with realistic model parameters show that this scheme is robust for interest rate valuation. [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 introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn^++.NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn^++ family of algorithms, that is, without requiring access to previously seen data. Learn^++.NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn^++.NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper. [ABSTRACT FROM AUTHOR]

Abstract: Two well-known methods for estimating statistical distributions in hydrology are the Method of Moments (MOMs) and the method of probability weighted moments (PWM). This paper is concerned with the case where a part of the sample is censored. One situation where this might occur is when systematic data (e.g. from gauges) are combined with historical data, since the latter are often only reported if they exceed a high threshold. For this problem, three previously derived estimators are the “B17B” estimator, which is a direct modification of MOM to allow for partial censoring; the “partial PWM estimator”, which similarly modifies PWM; and the “expected moments algorithm” estimator, which improves on B17B by replacing a sample adjustment of the censored-data moments with a population adjustment. The present paper proposes a similar modification to the PWM estimator, resulting in the “expected probability weighted moments (EPWM)” estimator. Simulation comparisons of these four estimators and also the maximum likelihood estimator show that the EPWM method is at least competitive with the other four and in many cases the best of the five estimators. [Copyright &y& Elsevier]

In a fork-join network each incoming job is split into tasks and the tasks are K K simultaneously assigned to parallel service stations for processing. For the distributions K of response times and queue lengths of fork-join networks, no explicit formulae are available. Existing methods provide only analytic approximations for the response time and the queue length distributions. The accuracy of such approximations may be difficult to justify for some complicated fork-join networks. In this paper we propose a perfect simulation method based on coupling from the past to generate exact realisations from the equilibrium of fork-join networks. Using the simulated realisations, Monte Carlo estimates for the distributions of response times and queue lengths of fork-join networks are obtained. Comparisons of Monte Carlo estimates and theoretical approximations are also provided. The efficiency of the sampling algorithm is shown theoretically and via simulation. [ABSTRACT FROM AUTHOR]

Abstract: Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brought back to the consideration of fundamental simulation algorithms, such as rejection sampling (RS). Unfortunately, the use of RS techniques demands the calculation of tight upper bounds for the ratio of the target probability density function (pdf) over the proposal density from which candidate samples are drawn. Except for the class of log-concave target pdf''s, for which an efficient algorithm exists, there are no general methods to analytically determine this bound, which has to be derived from scratch for each specific case. In this paper, we introduce new schemes for (a) obtaining upper bounds for likelihood functions and (b) adaptively computing proposal densities that approximate the target pdf closely. The former class of methods provides the tools to easily sample from a posteriori probability distributions (that appear very often in signal processing problems) by drawing candidates from the prior distribution. However, they are even more useful when they are exploited to derive the generalized adaptive RS (GARS) algorithm introduced in the second part of the paper. The proposed GARS method yields a sequence of proposal densities that converge towards the target pdf and enable a very efficient sampling of a broad class of probability distributions, possibly with multiple modes and non-standard forms. We provide some simple numerical examples to illustrate the use of the proposed techniques, including an example of target localization using range measurements, often encountered in sensor network applications. [Copyright &y& Elsevier]

Abstract: This paper presents a new method to reduce uncertainties in reservoir simulation models using observed data and sampling techniques. The proposed methodology is able to deal with problems with a high number of reservoir uncertain attributes and includes the development of a probability redistribution algorithm using observed data. The use of Latin Hypercube technique in the construction of uncertainty curves that is a quantitative representation of the overall uncertainty of the problem studied was also proposed. Based on new probability distributions, selective samples are carried out through the Latin Hypercube technique. The methodology was evaluated using two case studies. The first one, used for validation purposes, is a simple reservoir with 8 attributes; the second one is a more complex case with 16 attributes. The results presented in the paper showed that the proposed methodology can be efficiently used in the integrated study of history matching and uncertainty analysis, providing a practical way to increase the reliability of prediction through reservoir simulation models reducing the uncertainty through observed data. [Copyright &y& Elsevier]

zobov (ZOnes Bordering On Voidness) is an algorithm that finds density depressions in a set of points, without any free parameters, or assumptions about shape. It uses the Voronoi tessellation to estimate densities, which it uses to find both voids and subvoids. It also measures probabilities that each void or subvoid arises from Poisson fluctuations. This paper describes thezobov algorithm, and the results from its application to the dark matter particles in a region of the Millennium simulation. Additionally, the paper points out an interesting high-density peak in the probability distribution of dark matter particle densities. [ABSTRACT FROM AUTHOR]

Blind deconvolution is a method of recovering transmitted signals from only received signals. The probability distribution method is one of the blind deconvolution methods. This method has two problems: it has slower convergence and its reliability is lower. In this paper, we propose a new algorithm for solving these two problems. The proposed algorithm is as follows. (1) It is based on the adaptive processing with each sample. (2) Kurtosis is adaptively estimated by recovered signals with each sample. (3) Cost function is decided by kurtosis. (4) Transmitted signals are recovered by received signals using decided cost function on the sample time. We confirm the validity of the new algorithm by computer simulation. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(1): 56–65, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20317 [ABSTRACT FROM AUTHOR]

Given two sequences of length n over a finite alphabet A of size ∣A∣ = d, the D2 statistic is the number of k-letter word matches between the two sequences. This statistic is used in bioinformatics for EST sequence database searches. Under the assumption of independent and identically distributed letters in the sequences, Lippert, Huang and Waterman (2002) raised questions about the asymptotic behavior of D2 when the alphabet is uniformly distributed. They expressed a concern that the commonly assumed normality may create errors in estimating significance. In this paper we answer those questions. Using Stein's method, we show that, for large enough k, the D2 statistic is approximately normal as n gets large. When k = 1, we prove that, for large enough d, the D2 statistic is approximately normal as n gets large. We also give a formula for the variance of D2 in the uniform case. [ABSTRACT FROM AUTHOR]

This paper deals with the blind separation of instantaneous mixtures of source signals using time-frequency distributions (TFDs). We propose iterative algorithms to perform the nonorthogonal zero diagonalization and/or joint diagonalization of given sets of matrices. As an application, we show that the source separation can be realized by applying one of these algorithms to a set of spatial quadratic TFD matrices corresponding only to the so-called cross-source terms and/or to the so-called autosource terms. The determination of the above matrices to be jointly decomposed requires first an automatic selection procedure of useful time-frequency points. Regarding this last point, we also propose a new selection procedure and a modification of an existing one and provide a comparison with other existing ones. The nonorthogonal joint diagonalization and/or zero diagonalization algorithm's main advantage is to not require (in the blind source separation context) a prewhitening stage, which allows them to work even with a class of correlated signals and provides generally improved separation performance. Finally, an analytical example and computer simulations are provided in order to illustrate the effectiveness of the proposed approach and to compare it with classical ones. [ABSTRACT FROM AUTHOR]

This paper addresses the estimation of multipath channels with long impulse response at low signal-to-noise ratio (SNR). The channel sparseness impulse response is modeled by means of a Bernoulli-Gaussian process. Then, the optimization of the resulting posterior distribution resorts to Monte Carlo Markov chain (MCMC) simulation techniques. Special attention is devoted to noise correlation involved by matched filtering: Taking this correlation into account in the algorithm derivation leads to significantly improved performance for both amplitude and time-delay estimation. The method is also extended to cope with Doppler frequency offsets. In particular, simultaneous paths with different Doppler offsets can be estimated. Cramér-Rao lower bounds (CRLBs) are derived and presented together with simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a new randomized search method called evolutionary random policy search (ERPS) for solving infinite-horizon discounted-cost Markov-decision-process (MDP) problems. The algorithm is particularly targeted at problems with large or uncountable action spaces. ERPS approaches a given MDP by iteratively dividing it into a sequence of smaller, random, sub-MDP problems based on information obtained from random sampling of the entire action space and local search. Each sub-MDP is then solved approximately by using a variant of the standard policy-improvement technique, where an elite policy is obtained. We show that the sequence of elite policies converges to an optimal policy with probability one. Some numerical studies are carried out to illustrate the algorithm and compare it with existing procedures. [ABSTRACT FROM AUTHOR]

This paper introduces a novel blind equalization algorithm for frequency-selective channels based on a Bayesian formulation of the problem and the sequential importance sampling (SIS) technique. SIS methods rely on building a Monte Carlo (MC) representation of the probability distribution of interest that consists of a set of samples (usually called particles) and associated weights computed recursively in time. We elaborate on this principle to derive blind sequential algorithms that perform maximum a posteriori (MAP) symbol detection without explicit estimation of the channel parameters. In particular, we start with a basic algorithm that only requires the a priori knowledge of the model order of the channel, but we subsequently relax this assumption and investigate novel procedures to handle model order uncertainty as well. The bit error rate (BER) performance of the proposed Bayesian equalizers is evaluated and compared with that of other equalizers through computer simulations. [ABSTRACT FROM AUTHOR]

Abstract: A straightforward application of an interacting particle system to estimate a rare event for switching diffusions fails to produce reasonable estimates within a reasonable amount of simulation time. To overcome this, a conditional “sampling per mode” algorithm has been proposed by Krystul in ; instead of starting the algorithm with particles randomly distributed, we draw in each mode, a fixed number particles and at each resampling step, the same number of particles is sampled for each visited mode. In this paper, we establish a law of large numbers as well as a central limit theorem for the estimate. [Copyright &y& Elsevier]

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We consider the distributed containment control of multiagent systems with multiple stationary leaders and noisy measurements. A stochastic approximation type and consensus-like algorithm is proposed to solve the containment control problem. We provide conditions under which all the followers can converge both almost surely and in mean square to the stationary convex hull spanned by the leaders. Simulation results are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the joint distribution function of the event time and a continuous mark variable based on censored data. More specifically, the event time is subject to current status censoring and the continuous mark is only observed in case inspection takes place after the event time. The nonparametric maximum likelihood estimator in this model is known to be inconsistent. We propose and study an alternative likelihood-based estimator, maximising a smoothed log-likelihood, hence called a maximum smoothed likelihood estimator (MSLE). This estimator is shown to be well defined and consistent, and a simple algorithm is described that can be used to compute it. The MSLE is compared with other estimators in a small simulation study. [ABSTRACT FROM PUBLISHER]

Abstract: We consider the problem of time-stepping/sampling for molecular and meso-scale particle dynamics. The aim of the work is to derive numerical time-stepping methods that generate samples exactly from the desired target temperature distribution. The numerical methods proposed in this paper rely on the well-known splitting of stochastic thermostat equations into conservative and fluctuation-dissipation parts. We propose a methodology to derive numerical approximation to the fluctuation-dissipation part that exactly samples from the underlying Boltzmann distribution. Our methodology applies to Langevin dynamics as well as Dissipative Particle Dynamics and, more generally, to arbitrary position dependent fluctuation-dissipation terms. A Metropolis criterion is introduced to correct for numerical inconsistency in the conservative dynamics part of the model. Shadow energies are used to increase the acceptance rate under the Metropolis criterion. We call the newly proposed method meso-GSHMC. [Copyright &y& Elsevier]

Abstract: Due to the hardness of solving the minimum spanning tree (MST) problem in stochastic environments, the stochastic MST (SMST) problem has not received the attention it merits, specifically when the probability distribution function (PDF) of the edge weight is not a priori known. In this paper, we first propose a learning automata-based sampling algorithm (Algorithm 1) to solve the MST problem in stochastic graphs where the PDF of the edge weight is assumed to be unknown. At each stage of the proposed algorithm, a set of learning automata is randomly activated and determines the graph edges that must be sampled in that stage. As the proposed algorithm proceeds, the sampling process focuses on the spanning tree with the minimum expected weight. Therefore, the proposed sampling method is capable of decreasing the rate of unnecessary samplings and shortening the time required for finding the SMST. The convergence of this algorithm is theoretically proved and it is shown that by a proper choice of the learning rate the spanning tree with the minimum expected weight can be found with a probability close enough to unity. Numerical results show that Algorithm 1 outperforms the standard sampling method. Selecting a proper learning rate is the most challenging issue in learning automata theory by which a good trade off can be achieved between the cost and efficiency of algorithm. To improve the efficiency (i.e., the convergence speed and convergence rate) of Algorithm 1, we also propose four methods to adjust the learning rate in Algorithm 1 and the resultant algorithms are called as Algorithm 2 through Algorithm 5. In these algorithms, the probabilistic distribution parameters of the edge weight are taken into consideration for adjusting the learning rate. Simulation experiments show the superiority of Algorithm 5 over the others. To show the efficiency of Algorithm 5, its results are compared with those of the multiple edge sensitivity method (MESM). The obtained results show that Algorithm 5 performs better than MESM both in terms of the running time and sampling rate. [Copyright &y& Elsevier]

Abstract: All utility companies strive to achieve the well-balanced distribution systems in order to improve system voltage regulation by means of equal load balancing of feeders and reducing power loss. Optimal reconfiguration is one of the best solutions to reach this goal. This paper presents a new combined method for optimal reconfiguration using a multi-objective function with fuzzy variables. This method considers both objectives of load balancing and loss reduction in the feeders. Since reconfiguration is a nonlinear optimization problem, the ant colony algorithm is employed for the optimized response in search space. This method has been applied on two IEEE 33-bus and 69-bus distribution systems. Simulation results confirm the effectiveness of the proposed method in comparison with other techniques for optimal reconfiguration. [Copyright &y& Elsevier]

Abstract: Node models for macroscopic simulation have attracted relatively little attention in the literature. Nevertheless, in dynamic network loading (DNL) models for congested road networks, node models are as important as the extensively studied link models. This paper provides an overview of macroscopic node models found in the literature, explaining both their contributions and shortcomings. A formulation defining a generic class of first order macroscopic node models is presented, satisfying a list of requirements necessary to produce node models with realistic, consistent results. Defining a specific node model instance of this class requires the specification of a supply constraint interaction rule and (optionally) node supply constraints. Following this theoretical discussion, specific macroscopic node model instances for unsignalized and signalized intersections are proposed. These models apply an oriented capacity proportional distribution of the available supply over the incoming links of a node. A computationally efficient algorithm to solve the node models exactly is included. [Copyright &y& Elsevier]

Abstract: Strapdown inertial navigation system (SINS) is presently used in several applications related to aerospace system and marine navigation. The accurate initial attitude is essential to ensure the precise determination of the position and attitude of the moving platform, which is usually calculated using initial alignment. When the vehicle is moored, the SINS inevitably experience disturbing motion. The method of ground coarse alignment, which is based on the assumption that SINS is on a stationary carrier with limited vibration, therefore cannot be used to perform the SINS mooring alignment. In this paper, a novel method processing the gyro and accelerometer measurements with infinite impulse response (IIR) digital low-pass filter to remove the high frequency noise is investigated for marine mooring alignment. Its algorithmic principle is described in details. The results obtained from both simulation and mooring experiment show that the attitude determined by this novel method can meet the accurate alignment. [ABSTRACT FROM AUTHOR]

Abstract: introduced a family of similarity indices. Members of this family are linear functions of the matching counts matrix [m ij ], where m ij is the number of common elements between the i th and j th clusters resulting from two clusterings of the same data set. derived the mean and variance for index and an index they called B k (which is actually attributed to ) under fixed marginal totals of the matching counts matrix and independence of the clustering algorithms. This paper generalizes the derivation of for the mean and variance to any member of the family which makes the problem of comparison of a wide family of indices much easier. Monte Carlo simulations are implemented to compare shapes, means and variances for nine members of the family for null case data (without clustering structure). Structured case simulations are implemented to evaluate the nine indices as tools for measuring cluster structure recovery. Data were generated from bivariate normal distributions. [Copyright &y& Elsevier]

Abstract: This paper studies the problem of the gossip consensus algorithm with real-valued and quantized data. We study the effect of the mixing parameter on the convergence rate of the proposed gossip consensus algorithm, and show when the proposed bounds are optimized with respect to the mixing parameter. For a gossip consensus algorithm with quantized data, we prove that it can achieve the consensus almost surely, and the expected value of the final states is equal to the average of the initial states. Moreover, we provide a result characterizing the convergence performance of the distance from consensus and make a comparison with the non-quantized gossip consensus algorithm. Finally, simulation results are provided to evaluate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Recently distributed adaptive estimation algorithms have been proposed as a solution to the issue of linear estimation over distributed networks. In all previous works, the performance of such algorithms is considered only for infinite-precision arithmetic implementation. In this paper we study the performance of distributed incremental least mean square (DILMS) estimation algorithm when it is implemented in finite-precision arithmetic. To this aim, we first derive the quantized version of the DILMS algorithm. Then a spatial–temporal energy conservation argument is used to derive theoretical expressions that evaluate the steady-state performance of individual nodes in the network. Simulation results show that there is a good match between the theory and simulation. [Copyright &y& Elsevier]

Abstract: The paper presents an approximation algorithm for evaluating cdf of time-to-failure for k-out-of-n systems with shared spare elements. The working and spare elements can have different arbitrary distributions of time-to-failure. Different spare elements can be in different standby modes: cold, hot and warm. The algorithm is based on a universal generating function approach. It is shown that for small k the algorithm outperforms simulation. [Copyright &y& Elsevier]

Abstract: Recently Sarhan and Balakrishnan [2007. A new class of bivariate distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527] introduced a new bivariate distribution using generalized exponential and exponential distributions. They discussed several interesting properties of this new distribution. Unfortunately, they did not discuss any estimation procedure of the unknown parameters. In this paper using the similar idea as of Sarhan and Balakrishnan [2007. A new class of bivariate distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527], we have proposed a singular bivariate distribution, which has an extra shape parameter. It is observed that the marginal distributions of the proposed bivariate distribution are more flexible than the corresponding marginal distributions of the Marshall–Olkin bivariate exponential distribution, Sarhan–Balakrishnan''s bivariate distribution or the bivariate generalized exponential distribution. Different properties of this new distribution have been discussed. We provide the maximum likelihood estimators of the unknown parameters using EM algorithm. We reported some simulation results and performed two data analysis for illustrative purposes. Finally we propose some generalizations of this bivariate model. [Copyright &y& Elsevier]

Abstract: In this paper we consider the estimation of a density function on the basis of a random stratified sample from weighted distributions. We propose a linear wavelet density estimator and prove its consistency. The behavior of the proposed estimator and its smoothed versions is eventually illustrated by simulated examples and a case study involving alcohol blood level in DUI cases. [Copyright &y& Elsevier]

Abstract: In this paper, we propose VQ methods based on ensemble learning algorithms Bagging and AdaBoost. The proposed methods consist of more than one weak learner, which are trained in parallel or sequentially. In Bagging, the weak learners are trained in parallel by using randomly selected data from a given data set. The output for Bagging is given as the average among the weak learners. In AdaBoost, the weak learners are sequentially trained. The first weak learner is trained by using randomly selected data from a given data set. For the second and later weak learners, the probability distribution of learning data is modified so that each weak learner focuses on data involving higher error for the previous weak one. The output for AdaBoost is given as the weighted average among the weak learners. The presented simulation results show that the proposed methods can achieve a good performance in shorter learning times than conventional ones such as K-means and NG. [Copyright &y& Elsevier]

Abstract: Modeling and simulation is essential for understanding complex ecological systems. However, knowledge of the structure and behavior of these systems is limited, and models must be revised frequently as our understanding of a system improves. Moreover, the dynamic, spatial distribution of activity in very large systems necessitates mapping natural mechanisms as logically as possible onto computer structures. This paper describes theoretical and algorithmic tools for building component-based models and simulations of dynamic spatial phenomena. These methods focus attention on and exploit the irregular distribution of activity in ecological processes. We use the DEVS formalism as the basis for a component-based approach to modeling spatially distributed systems. DEVS is a mathematical theory of discrete-event systems that is well suited for describing large systems that are described by small parts with irregular, short-range interactions. This event-based approach to modeling leads naturally to efficient simulations algorithms which focus on the active parts of a large model. Ecological modeling benefits from these efficient the simulation algorithms and the reusability of the model’s basic components. Our event-based method is demonstrated with a physics-based model of fire spread. [Copyright &y& Elsevier]

FastICA is one of the most popular algorithms for independent component analysis (ICA), demixing a set of statistically independent sources that have been mixed linearly. A key question is how accurate the method is for finite data samples. We propose an improved version of the FastICA algorithm which is asymptotically efficient, i.e., its accuracy given by the residual error variance attains the Cramér-Rao lower bound (CRB). The error is thus as small as possible. This result is rigorously proven under the assumption that the probability distribution of the independent signal components belongs to the class of generalized Gaussian (GG) distributions with parameter α, denoted GG(α) for α > 2. We name the algorithm efficient FastICA (EFICA). Computational complexity of a Matlab implementation of the algorithm is shown to be only slightly (about three times) higher than that of the standard symmetric FastICA. Simulations corroborate these claims and show superior performance of the algorithm compared with algorithm JADE of Cardoso and Souloumiac and nonparametric ICA of Boscolo et al. on separating sources with distribution GG(α) with arbitrary α, as well as on sources with bimodal distribution, and a good performance in separating linearly mixed speech signals. [ABSTRACT FROM AUTHOR]

Methods for stochastic simulation of sample functions have increasingly addressed the preservation of both spectral and probabilistic contents to offer an accurate description of the dynamic behavior of system input for reliability analysis. This study presents an efficient, flexible and easily applied stochastic non-Gaussian simulation method capable of reliably converging to a target power spectral density function and marginal probability density function, or a close relative thereof. Several existing spectral representation-based non-Gaussian simulation algorithms are first summarized. The new algorithm is then presented and compared with these methods to demonstrate its efficacy. The advantages and limitations of the new method are highlighted and shown to complement those of the existing algorithms. [ABSTRACT FROM AUTHOR]