In this article, an overview of Bayesian methods for sequential simulation from posterior distributions nonlinear and non-Gaussian dynamic systems is presented. The focus mainly laid on Monte Carlo methods, which are based particle representations probability densities can be seamlessly generalized to any state-space representation. Within context, a unified framework the various Particle Filte...

This paper develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use latent process distributional transformation to construct with very flexible correlation features that can have any pre-specified marginal distribution, including classical Poisson, generalized negative binomial, binomial structures. pseudo-likelihood implied Y...

DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream net...

The paper introduces a new non-Gaussian simulation method that matches a target power spectrum and probability information. Numerical studies demonstrate applicability, convergence, and stationary properties. The method is shown to encompass a larger envelope of spectral/probabilistic descriptions than correlation distortion based methods. Specifically, the method is not constrained to relative...

This paper proposes a hybrid method to find cumulative distribution function (CDF) of completion time of GERT-type networks (GTN) which have no loop and have only exclusive-or nodes. Proposed method is cre-ated by combining an analytical transformation with Gaussian quadrature formula. Also the combined crude Monte Carlo simulation and combined conditional Monte Carlo simulation are developed a...

A current problem of practical significance is the analysis of large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between “spins”. The spins represent multilevel discretizations of envi...

The length of equal minimal and maximal blocks has eected on logarithm-scale logarithm against sequential function on variance and bias of de-trended uctuation analysis, by using Quasi Monte Carlo(QMC) simulation and Cholesky decompositions, minimal block couple and maximal are founded which are minimum the summation of mean error square in Horest power.