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Stochastic Variables (stochastic + variable)
Selected AbstractsDeterminants of inselberg floras in arid Nama Karoo landscapesJOURNAL OF BIOGEOGRAPHY, Issue 10 2001Antje Burke Aim This study investigated the relationship between inselberg floras in floristic and functional terms and their correlation with environmental variables at macro-scale and landscape level. Location Four inselberg landscapes in Namibia's arid Nama Karoo in southern Africa were selected. Methods Plant surveys were undertaken over a 3-year period and species composition, growth form and dispersal spectra were used as measures for floristic and functional composition. Canonical correspondence analysis (CCA) was employed as the main tool to explain the perceived patterns. Results Regarding floristic affinities, inselberg floras formed distinct groups per study area and thus geographical position, with strong correlations between inselbergs within a particular study area. Neither growth form nor dispersal spectra closely resembled the pattern that emerged in the ordination of floristic composition. The influence of geographical position lessened when functional rather than floristic measures were introduced in the analysis. Main conclusions (1) On landscape and macro-scale, floristic composition of inselberg floras was largely determined by geographical position, geology, elevation, habitat diversity, rock outcrop in the surrounding and surface area of inselbergs. (2) Environmental variables operating at landscape level had greater influence on functional composition than on floristic composition. (3) Stochastic variables were more important in shaping the flora of these arid Nama Karoo inselbergs than deterministic processes such as niche relations and competition. [source] Motor unit number estimation using reversible jump Markov chain Monte Carlo methodsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2007P. G. Ridall Summary., We present an application of reversible jump Markov chain Monte Carlo sampling from the field of neurophysiology where we seek to estimate the number of motor units within a single muscle. Such an estimate is needed for monitoring the progression of neuromuscular diseases such as amyotrophic lateral sclerosis. Our data consist of action potentials that were recorded from the surface of a muscle in response to stimuli of different intensities applied to the nerve supplying the muscle. During the gradual increase in intensity of the stimulus from the threshold to supramaximal, all motor units are progressively excited. However, at any given submaximal intensity of stimulus, the number of units that are excited is variable, because of random fluctuations in axonal excitability. Furthermore, the individual motor unit action potentials exhibit variability. To account for these biological properties, Ridall and co-workers developed a model of motor unit activation that is capable of describing the response where the number of motor units, N, is fixed. The purpose of this paper is to extend that model so that the possible number of motor units, N, is a stochastic variable. We illustrate the elements of our model, show that the results are reproducible and show that our model can measure the decline in motor unit numbers during the course of amyotrophic lateral sclerosis. Our method holds promise of being useful in the study of neurogenic diseases. [source] State estimation for time-delay systems with probabilistic sensor gain reductionsASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2008Xiao He Abstract This paper presents a new state estimation problem for a class of time-delay systems with probabilistic sensor gain faults. The sensor gain reductions are described by a stochastic variable that obeys the uniform distribution in a known interval [,, ,], which is a natural reflection of the probabilistic performance deterioration of sensors when gain reduction faults occur. Attention is focused on the design of a state estimator such that for all possible sensor faults and all external disturbances, the filtering error dynamic is asymptotically mean-square stable as well as fulfils a prescribed disturbance attenuation level. The existence of desired filters is proved to depend on the feasibility of a certain linear matrix inequality (LMI), and a numerical example is given to illustrate the effectiveness of the proposed design approach. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd. [source] Distribution of Aggregate Utility Using Stochastic Elements of Additive Multiattribute Utility ModelsDECISION SCIENCES, Issue 2 2000Herbert Moskowitz ABSTRACT Conventionally, elements of a multiattribute utility model characterizing a decision maker's preferences, such as attribute weights and attribute utilities, are treated as deterministic, which may be unrealistic because assessment of such elements can be imprecise and erroneous, or differ among a group of individuals. Moreover, attempting to make precise assessments can be time consuming and cognitively demanding. We propose to treat such elements as stochastic variables to account for inconsistency and imprecision in such assessments. Under these assumptions, we develop procedures for computing the probability distribution of aggregate utility for an additive multiattribute utility function (MAUF), based on the Edgeworth expansion. When the distributions of aggregate utility for all alternatives in a decision problem are known, stochastic dominance can then be invoked to filter inferior alternatives. We show that, under certain mild conditions, the aggregate utility distribution approaches normality as the number of attributes increases. Thus, only a few terms from the Edgeworth expansion with a standard normal density as the base function will be sufficient for approximating an aggregate utility distribution in practice. Moreover, the more symmetric the attribute utility distributions, the fewer the attributes to achieve normality. The Edgeworth expansion thus can provide a basis for a computationally viable approach for representing an aggregate utility distribution with imprecisely specified attribute weights and utilities assessments (or differing weights and utilities across individuals). Practical guidelines for using the Edgeworth approximation are given. The proposed methodology is illustrated using a vendor selection problem. [source] eXtended Stochastic Finite Element Method for the numerical simulation of heterogeneous materials with random material interfacesINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 10 2010A. Nouy Abstract An eXtended Stochastic Finite Element Method has been recently proposed for the numerical solution of partial differential equations defined on random domains. This method is based on a marriage between the eXtended Finite Element Method and spectral stochastic methods. In this article, we propose an extension of this method for the numerical simulation of random multi-phased materials. The random geometry of material interfaces is described implicitly by using random level set functions. A fixed deterministic finite element mesh, which is not conforming to the random interfaces, is then introduced in order to approximate the geometry and the solution. Classical spectral stochastic finite element approximation spaces are not able to capture the irregularities of the solution field with respect to spatial and stochastic variables, which leads to a deterioration of the accuracy and convergence properties of the approximate solution. In order to recover optimal convergence properties of the approximation, we propose an extension of the partition of unity method to the spectral stochastic framework. This technique allows the enrichment of approximation spaces with suitable functions based on an a priori knowledge of the irregularities in the solution. Numerical examples illustrate the efficiency of the proposed method and demonstrate the relevance of the enrichment procedure. Copyright © 2010 John Wiley & Sons, Ltd. [source] A genetic algorithm and the Monte Carlo method for stochastic job-shop schedulingINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 6 2003Y. Yoshitomi Abstract This paper proposes a method for solving stochastic job-shop scheduling problems using a hybrid of a genetic algorithm in uncertain environments and the Monte Carlo method. First, the genetic algorithm in uncertain environments is applied to stochastic job-shop scheduling problems where the processing times are treated as stochastic variables. The Roulette strategy is adopted for selecting the optimum solution having the minimum expected value for makespan. Applying crossover based on Giffler and Thompson's algorithm results in two offspring inheriting the ancestor's characteristics as the operation completion times averaged up to the parent's generation. Individuals having very high frequency through all generations are selected as the good solutions. Second, the Monte Carlo method is effectively used for finding out the approximately optimum solution among these good solutions. [source] Sampled-data H, control for networked systems with random packet dropouts,ASIAN JOURNAL OF CONTROL, Issue 4 2010Xiaosheng Fang Abstract This paper is concerned with the H, control problem for networked control systems (NCSs) with random packet dropouts. The NCS is modeled as a sampled-data system which involves a continuous plant, a digital controller, an event-driven holder and network channels. In this model, two types of packet dropouts in the sensor-to-controller (S/C) side and controller-to-actuator (C/A) side are both considered, and are described by two mutually independent stochastic variables satisfying the Bernoulli binary distribution. By applying an input/output delay approach, the sampled-data NCS is transformed into a continuous time-delay system with stochastic parameters. An observer-based control scheme is designed such that the closed-loop NCS is stochastically exponentially mean-square stable and the prescribed H, disturbance attenuation level is also achieved. The controller design problem is transformed into a feasibility problem for a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed design method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | |||||||||||||