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EXAMPLES OF APPLYING MONTE CARLO SIMULATIONS IN THE FIELD OF MEASUREMENT UNCERTAINTIES OF THE STANDARD OF LENGTH
, 2003
"... have been increasingly applied in the field of estimating the measurement uncertainties. The MCS method is based on random number generation from the probability density functions for each input value and forming of experimental probability density function of the output value. The paper presents ca ..."
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calculation examples of the measurement uncertainty in calibration of the standard of length by using the MCS method. The MCS method is implemented for validation of the realised measurement uncertainties by GUM method in calibrating the standard of length.
A Brief Note On Maximum Realisable MCMC Classifiers
, 1999
"... We discuss a strategy for estimating and combining classiers via ROC curves, decision analysis theory and MCMC simulation. This paradigm also allows us to select samples from an MCMC run in a parsimonious fashion. Each ROC curve, corresponds to a sample (classier) obtained with a full Bayesian mo ..."
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Cited by 1 (1 self)
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maximum realisable classiers and selecting samples from a Markov chain Monte Carlo (MCMC) r...
Threedimensional dynamic Monte Carlo simulations of elastic actinlike ratchets
, 2005
"... We present threedimensional MonteCarlo dynamic simulations of the growth of a semiflexible fibre against a fluctuating obstacle. The natural reference for our numerical study are the elastic and brownian ratchet models previously analysed semianalytically. We find that the decay of the velocity ..."
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Cited by 3 (0 self)
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We present threedimensional MonteCarlo dynamic simulations of the growth of a semiflexible fibre against a fluctuating obstacle. The natural reference for our numerical study are the elastic and brownian ratchet models previously analysed semianalytically. We find that the decay of the velocity
Markov Chain Monte Carlo Posterior Sampling With The Hamiltonian Method
 Proc. Sensitivity Analysis of Model Output
, 2001
"... INTRODUCTION A major advantage of Bayesian data analysis is that provides a characterisation of the uncertainty in the model parameters estimated from a given set of measurements in the form of a posterior probability distribution [1]. When the analysis involves a complicated physical phenomenon, t ..."
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, is to employ the Markov Chain Monte Carlo technique. The goal of MCMC is to generate a sequence random of parameter x samples from a target pdf (probability density function), (x). In Bayesian analysis, this sequence corresponds to a set of model realisations that follow the posterior distribution [2
Probing cosmological parameters with the CMB: Forecasts from full Monte Carlo simulations,” JCAP 0610
, 2006
"... Abstract. The Fisher matrix formalism has in recent times become the standard method for predicting the precision with which various cosmological parameters can be extracted from future data. This approach is fast, and generally returns accurate estimates for the parameter errors when the individual ..."
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Cited by 31 (6 self)
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Carlo simulations. The latter method is based on the publicly available CosmoMC code, but uses synthetic realisations of data sets anticipated for future experiments. We focus on prospective cosmic microwave background (CMB) data from the Planck satellite, with or without CMB lensing information
Generating Realisations of Stationary Gaussian Random Fields by Circulant Embedding.
"... Random fields are families of random variables, indexed by a ddimensional parameter x with d> 1. They are important in many applications and are used, for example, to model properties of biological tissue, velocity fields in turbulent flows and permeability coefficients of rocks. Mark 24 of the ..."
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of Z(x, ω) so that statistical analysis of the solution can be performed (e.g., using Monte Carlo methods).
Markov chain Monte Carlo sampling of a normal distribution from an exponential distribution
, 2004
"... The following is a simple example to show two important properties of a Markov chain Monte Carlo (MCMC) sampler and to illustrate the basic functionality of the method and issues relating to it’s usage. A Markov chain is a series where the realisation of the next element in the series, Y, is depende ..."
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The following is a simple example to show two important properties of a Markov chain Monte Carlo (MCMC) sampler and to illustrate the basic functionality of the method and issues relating to it’s usage. A Markov chain is a series where the realisation of the next element in the series, Y
UDC 338:519.8 ESTIMATION OF MARKET RISK BY MEANS OF INDICATORS VAR AND SHORTFALL
"... Possibility of application of indicators VaR and Shortfall for an estimation and management of market risk is under consideration in this article. We suppose that the portfolio from shares of the several companies is created. It is necessary to estimate market risk both for all portfolios as a whole ..."
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whole, and for its emitters. The numerical realisation is based on oneday historical data of a course of 6 shares of the different companies on RTS in 2007 is carried out.The number of modeled scenarios, which we should use for finding a Varindicator with MonteCarlo method is analysed. Found Va
unknown title
"... This paper presents a stochastic approach to delineate the capture zones for the well field ‘Het Rot ’ at Nieuwrode (Belgium). The conductivity of the production aquifer is modelled as a random space function (RSF). For the conductivity of the overlying semipervious unit and the top phreatic aquife ..."
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of this layer using Bayes ’ theorem, yielding posterior parameter distributions. Conditional realisations of the production aquifer conductivity field are generated using parameter sets sampled by Monte Carlo from the posterior distributions of the structural parameters. The realisations are combined
2012): “Econometric analysis of multivariate realized QML: Estimation of the covariation of equity prices under asynchronous trading,” Discussion paper
"... Abstract Estimating the covariance between assets using high frequency data is challenging due to market microstructure effects and asynchronous trading. In this paper we develop a multivariate realised quasilikelihood (QML) approach, carrying out inference as if the observations arise from an asy ..."
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Cited by 10 (1 self)
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a relatively simple EM algorithm which scales well with the number of assets. We derive the theoretical properties of the estimator and prove that it achieves the efficient rate of convergence. The estimator is also analysed using Monte Carlo methods and applied to equity data with varying levels
Results 11  20
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70