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Monte Carlo Techniques (monte + carlo_techniques)
Selected AbstractsBayesian hierarchical models in ecological studies of health,environment effectsENVIRONMETRICS, Issue 2 2003Sylvia Richardson Abstract We describe Bayesian hierarchical models and illustrate their use in epidemiological studies of the effects of environment on health. The framework of Bayesian hierarchical models refers to a generic model building strategy in which unobserved quantities (e.g. statistical parameters, missing or mismeasured data, random effects, etc.) are organized into a small number of discrete levels with logically distinct and scientifically interpretable functions, and probabilistic relationships between them that capture inherent features of the data. It has proved to be successful for analysing many types of complex epidemiological and biomedical data. The general applicability of Bayesian hierarchical models has been enhanced by advances in computational algorithms, notably those belonging to the family of stochastic algorithms based on Markov chain Monte Carlo techniques. In this article, we review different types of design commonly used in studies of environment and health, give details on how to incorporate the hierarchical structure into the different components of the model (baseline risk, exposure) and discuss the model specification at the different levels of the hierarchy with particular attention to the problem of aggregation (ecological) bias. Copyright © 2003 John Wiley & Sons, Ltd. [source] Random porosity fields and their influence on the stability of granular mediaINTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 10 2008José E. Andrade Abstract It is well established that the mechanical behavior of granular media is strongly influenced by the media's microstructure. In this work, the influence of the microstructure is studied by integrating advances in the areas of geostatistics and computational plasticity, by spatially varying the porosity on samples of sand. In particular, geostatistical tools are used to characterize and simulate random porosity fields that are then fed into a nonlinear finite element model. The underlying effective mechanical response of the granular medium is governed by a newly developed elastoplastic model for sands, which readily incorporates spatial variability in the porosity field at the meso-scale. The objective of this study is to assess the influence of heterogeneities in the porosity field on the stability of sand samples. One hundred and fifty isotropic and anisotropic samples of dense sand are failed under plane-strain compression tests using Monte Carlo techniques. Results from parametric studies indicate that the axial strength of a specimen is affected by both the degree and orientation of anisotropy in heterogeneous porosity values with anisotropy orientation having a dominant effect, especially when the bands of high porosity are aligned with the natural orientation of shear banding in the specimen. Copyright © 2007 John Wiley & Sons, Ltd. [source] Vibrational,rotational energies of all H2 isotopomers using Monte Carlo methodsINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 8 2006S. A. Alexander Abstract Using variational Monte Carlo techniques, we have computed several of the lowest rotational,vibrational energies of all the hydrogen molecule isotopomers (H2, HD, HT, D2, DT, and T2). These calculations do not require the excited states to be explicitly orthogonalized. We have examined both the usual Gaussian wave function form as well as a rapidly convergent Padé form. The high-quality potential energy surfaces used in these calculations are taken from our earlier work and include the Born,Oppenheimer energy, the diagonal correction to the Born,Oppenheimer approximation, and the lowest-order relativistic corrections at 24 internuclear points. Our energies are in good agreement with those determined by other methods. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2006 [source] Adaptive approach for nonlinear sensitivity analysis of reaction kineticsJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 9 2005Illia Horenko Abstract We present a unified approach for linear and nonlinear sensitivity analysis for models of reaction kinetics that are stated in terms of systems of ordinary differential equations (ODEs). The approach is based on the reformulation of the ODE problem as a density transport problem described by a Fokker,Planck equation. The resulting multidimensional partial differential equation is herein solved by extending the TRAIL algorithm originally introduced by Horenko and Weiser in the context of molecular dynamics (J. Comp. Chem. 2003, 24, 1921) and discussed it in comparison with Monte Carlo techniques. The extended TRAIL approach is fully adaptive and easily allows to study the influence of nonlinear dynamical effects. We illustrate the scheme in application to an enzyme-substrate model problem for sensitivity analysis w.r.t. to initial concentrations and parameter values. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 941,948, 2005 [source] Fine-scale spatial structure in a grassland community: quantifying the plant's-eye viewJOURNAL OF ECOLOGY, Issue 1 2002D. W. Purves Summary 1The fine-scale spatial patterns of Agrostis stolonifera, Holcus lanatus and Lolium perenne were recorded in an English lowland grassland as presence/absence maps from 400-cell quadrats at two different scales (2 × 2 cm or 8 × 8 cm cells). 2Local spatial structure in these patterns was quantified using spatial covariance functions. Distance- and direction-dependent components were examined separately for both intra- and interspecific patterns. The significance of departures from randomness was determined using Monte Carlo techniques. 3The smaller-scale data showed that all three species were significantly aggregated, Agrostis to a greater distance (8 cm) than Holcus or Lolium(4 cm). The intensity of aggregation decreased in the order Lolium > Holcus > Agrostis. The larger-scale data suggested that this aggregation extended to greater distances, and that it was most intense in Agrostis. 4Despite the lack of visual directionality in the environment, Agrostis showed a directional pattern at both scales, with Lolium varying in the same direction at the larger scale. 5Only Agrostis and Lolium showed a significant interspecific relationship (segregated to 2 cm at the small scale, but aggregated to 8 cm at the larger scale). There was no evidence of directionality in the interspecific components of pattern. 6The nature of spatial structure appears to depend on the scale of observation, but the smaller-scale data are more likely to provide a biologically interpretable measure of local spatial structure in this community. [source] OPTIMIZING ELECTRON BEAM IRRADIATION OF "TOMMY ATKINS" MANGOES (MANGIFERA INDICA L.)JOURNAL OF FOOD PROCESS ENGINEERING, Issue 4 2007MARIA A. MORENO ABSTRACT We determined the optimum irradiation treatment for decontamination of physiologically mature fresh "Tommy Atkins" mangoes, without detriment to the fruits' sensory and chemical properties. Mangoes were irradiated at 1.0, 1.5 and 3.1 kGy using a 10-MeV linear accelerator (14-kW LINAC, double beam mode). Mangoes were stored for 21 days at 12C and 62.7% relative humidity with nonirradiated fruits as controls. Dose distribution within the fruit was determined using Monte Carlo techniques. Irradiation did not affect the overall sensory quality of mangoes at doses up to 1.5 kGy. Only fruits irradiated at 3.1 kGy were unacceptable by the panelists. Irradiation at 3.1 kGy enhanced the fruit's aroma characteristics. Irradiation at all levels caused a significant (P , 0.05) decrease (,50,70%) in ascorbic acid content by the end of storage. Mangoes irradiated at 1.5 and 3.1 kGy had slightly higher levels of phenolics than the control (27.4 and 18.3%, respectively). E-beam irradiation of Tommy Atkins mangoes up to 3.0 kGy causes no detriment to the fruit's overall sensory and chemical quality. [source] Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategyJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 3 2000Francesca Dominici Reports over the last decade of association between levels of particles in outdoor air and daily mortality counts have raised concern that air pollution shortens life, even at concentrations within current regulatory limits. Criticisms of these reports have focused on the statistical techniques that are used to estimate the pollution,mortality relationship and the inconsistency in findings between cities. We have developed analytical methods that address these concerns and combine evidence from multiple locations to gain a unified analysis of the data. The paper presents log-linear regression analyses of daily time series data from the largest 20 US cities and introduces hierarchical regression models for combining estimates of the pollution,mortality relationship across cities. We illustrate this method by focusing on mortality effects of PM10 (particulate matter less than 10 ,m in aerodynamic diameter) and by performing univariate and bivariate analyses with PM10 and ozone (O3) level. In the first stage of the hierarchical model, we estimate the relative mortality rate associated with PM10 for each of the 20 cities by using semiparametric log-linear models. The second stage of the model describes between-city variation in the true relative rates as a function of selected city-specific covariates. We also fit two variations of a spatial model with the goal of exploring the spatial correlation of the pollutant-specific coefficients among cities. Finally, to explore the results of considering the two pollutants jointly, we fit and compare univariate and bivariate models. All posterior distributions from the second stage are estimated by using Markov chain Monte Carlo techniques. In univariate analyses using concurrent day pollution values to predict mortality, we find that an increase of 10 ,g m -3 in PM10 on average in the USA is associated with a 0.48% increase in mortality (95% interval: 0.05, 0.92). With adjustment for the O3 level the PM10 -coefficient is slightly higher. The results are largely insensitive to the specific choice of vague but proper prior distribution. The models and estimation methods are general and can be used for any number of locations and pollutant measurements and have potential applications to other environmental agents. [source] Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo methodJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2000C. P. Robert Hidden Markov models form an extension of mixture models which provides a flexible class of models exhibiting dependence and a possibly large degree of variability. We show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number of components of a hidden Markov model in a Bayesian framework. We employ a mixture of zero-mean normal distributions as our main example and apply this model to three sets of data from finance, meteorology and geomagnetism. [source] A continuous latent spatial model for crack initiation in bone cementJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2008Elizabeth A. Heron Summary., Hip replacements rovide a means of achieving a higher quality of life for individuals who have, through aging or injury, accumulated damage to their natural joints. This is a very common operation, with over a million people a year benefiting from the procedure. The replacements themselves fail mainly as a result of the mechanical loosening of the components of the artificial joint due to damage accumulation. This damage accumulation consists of the initiation and growth of cracks in the bone cement which is used to fixate the replacement in the human body. The data come from laboratory experiments that are designed to assess the effectiveness of the bone cement in resisting damage. We examine the properties of the bone cement, with the aim being to estimate the effect that both observable and unobservable spatially varying factors have on causing crack initiation. To do this, an explicit model for the damage process is constructed taking into account the tension and compression at different locations in the specimens. A gamma random field is used to model any latent spatial factors that may be influential in crack initiation. Bayesian inference is carried out for the parameters of this field and related covariates by using Markov chain Monte Carlo techniques. [source] A spatial model for the needle losses of pine-trees in the forests of Baden-Württemberg: an application of Bayesian structured additive regressionJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2007Nicole H. Augustin Summary., The data that are analysed are from a monitoring survey which was carried out in 1994 in the forests of Baden-Württemberg, a federal state in the south-western region of Germany. The survey is part of a large monitoring scheme that has been carried out since the 1980s at different spatial and temporal resolutions to observe the increase in forest damage. One indicator for tree vitality is tree defoliation, which is mainly caused by intrinsic factors, age and stand conditions, but also by biotic (e.g. insects) and abiotic stresses (e.g. industrial emissions). In the survey, needle loss of pine-trees and many potential covariates are recorded at about 580 grid points of a 4 km × 4 km grid. The aim is to identify a set of predictors for needle loss and to investigate the relationships between the needle loss and the predictors. The response variable needle loss is recorded as a percentage in 5% steps estimated by eye using binoculars and categorized into healthy trees (10% or less), intermediate trees (10,25%) and damaged trees (25% or more). We use a Bayesian cumulative threshold model with non-linear functions of continuous variables and a random effect for spatial heterogeneity. For both the non-linear functions and the spatial random effect we use Bayesian versions of P -splines as priors. Our method is novel in that it deals with several non-standard data requirements: the ordinal response variable (the categorized version of needle loss), non-linear effects of covariates, spatial heterogeneity and prediction with missing covariates. The model is a special case of models with a geoadditive or more generally structured additive predictor. Inference can be based on Markov chain Monte Carlo techniques or mixed model technology. [source] Low and high order light scattering in particulate mediaLASER PHYSICS LETTERS, Issue 8 2004I. V. Meglinski Abstract We present the results of a theoretical study providing details of propagation of laser radiation within disperse randomly inhomogeneous intermediately single-to-multiple scattering media. A quantitative analysis of scattering orders in the transition from single to multiple scattering is presented. Crossed source-detector fiber optics geometry used to separate the intensity of single scattering from higher scattering orders. The results demonstrate good agreement between analytical and Monte Carlo techniques. This validates the use of the Monte Carlo approach in the intermediate single-to-multiple scattering regime. The method used can be applied to verify analytical results against experiment via the Monte Carlo calculations that include imperfections of the experiment. (© 2004 by ASTRO, Ltd. Published exclusively by WILEY-VCH Verlag GmbH & Co. KGaA) [source] Shielding Effects in Polymer-Polymer Reactions, 1MACROMOLECULAR THEORY AND SIMULATIONS, Issue 6 2007Z-RAFT Star Polymerization of Four-Arm Stars Abstract Shielding effects of the surrounding arms and chains on the reactive centers taking part in RAFT four-arm star polymerization following the Z-group approach are calculated by means of exact enumeration of star/chain samples prepared by Monte Carlo techniques. The shielding effect, which can be relieved when using expanded core moieties, increases with increasing chain (arm) lengths. This leads to a reduction of the contact probability according to a power law with an exponent of ,0.4 to ,0.45. Additionally, characteristic chain properties and shape parameters are calculated as a function of the distance between the center of the star and the end of the linear chain in order to gain deeper insight into the mechanism of contact formation preceding the actual reaction. [source] NORTH ATLANTIC RIGHT WHALE DISTRIBUTION IN RELATION TO SEA-SURFACE TEMPERATURE IN THE SOUTHEASTERN UNITED STATES CALVING GROUNDSMARINE MAMMAL SCIENCE, Issue 2 2006Chérie A. Keller Abstract Standardized aerial surveys were used to document the winter (December,March) distribution of North Atlantic right whales in their calving area off the coasts of Georgia and northeastern Florida (1991,1998). Survey data were collected within four survey zones in and adjacent to federally designated critical habitat. These data, including whale-sighting locations and sampling effort, were used to describe right whale distribution in relation to sea-surface temperature (SST) from satellite-derived images. Locations where whales were sighted (n= 609) had an overall mean SST of 14.3°C ± 2.1° (range 8°,22°C). Data from two survey zones having sufficient data (including the "early warning system" (EWS) zone and the Florida nearshore) were pooled by season and stratified by month to investigate changes in monthly ambient SST and fine-scale distribution patterns of right whales in relation to SST within spatially explicit search areas. Using Monte Carlo techniques, SSTs and latitudes (means and standard deviations) of locations where whales were sighted were compared to a sampling distribution of each variable derived from daily-search areas. Overall, results support a nonrandom distribution of right whales in relation to SST: during resident months (January and February), whales exhibited low variability in observed SST and a suggested southward shift in whale distribution toward warmer SSTs in the EWS zone; while in the relatively warmer and southernmost survey zone (Florida nearshore), right whales were concentrated in the northern, cooler portion. Our results support that warm Gulf Stream waters, generally found south and east of delineated critical habitat, represent a thermal limit for right whales and play an important role in their distribution within the calving grounds. These results affirm the inclusion of SST in a multivariate predictive model for right whale distribution in their southeastern habitat. [source] Geo-additive models of childhood undernutrition in three sub-Saharan African countriesPOPULATION, SPACE AND PLACE (PREVIOUSLY:-INT JOURNAL OF POPULATION GEOGRAPHY), Issue 5 2009Ngianga-Bakwin Kandala Abstract We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighbouring countries in southern Africa, using the 1992 Demographic and Health Surveys. In particular, we estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use geo-additive regression models to flexibly model the effects of selected socioeconomic covariates and spatial effects. Inference is fully Bayesian based on recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm findings from the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt transcending boundaries and running from southern Tanzania to northeastern Zambia which exhibits much worse undernutrition. These findings have important implications for planning, as well as in the search for left-out variables that might account for these residual spatial patterns. Copyright © 2009 John Wiley & Sons, Ltd. [source] The Effect of Time-Series and Cross-Sectional Heterogeneity on Panel Unit Root Test PowerTHE JOURNAL OF FINANCIAL RESEARCH, Issue 3 2002John M. Geppert Abstract Panel unit root tests represent a significant advancement in addressing the low power of unit root tests by exploiting cross-sectional and time-series information. In this article we employ Monte Carlo techniques to quantify the power improvements due to cross-sectional information and assess test sensitivity to heterogeneous data. Pooling the data alleviates negative effects of slowly adjusting equilibrium relations as well as persistence in the forcing variable. However, if the panel contains a mixture of unit root and stationary series, the power of the test decreases substantially and the interpretation of the results becomes tenuous. [source] Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease StageBIOMETRICS, Issue 3 2004Naijun Sha Summary Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis. [source] Modeling Human Fertility in the Presence of Measurement ErrorBIOMETRICS, Issue 1 2000David B. Dunson Summary. The probability of conception in a given menstrual cycle is closely related to the timing of intercourse relative to ovulation. Although commonly used markers of time of ovulation are known to be error prone, most fertility models assume the day of ovulation is measured without error. We develop a mixture model that allows the day to be misspecified. We assume that the measurement errors are i.i.d. across menstrual cycles. Heterogeneity among couples in the per cycle likelihood of conception is accounted for using a beta mixture model. Bayesian estimation is straightforward using Markov chain Monte Carlo techniques. The methods are applied to a prospective study of couples at risk of pregnancy. In the absence of validation data or multiple independent markers of ovulation, the identifiability of the measurement error distribution depends on the assumed model. Thus, the results of studies relating the timing of intercourse to the probability of conception should be interpreted cautiously. [source] | ||||||||||||||||