Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, ...

truncated gaussian simulation (tgs) is a well-known method to generate realizations of the ore domains located in a spatial sequence. in geostatistical framework geological domains are normally utilized for stationary assumption. the ability to measure the uncertainty in the exact locations of the boundaries among different geological units is a common challenge for practitioners. as a simple a...

Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the sequential Monte-Carlo (SMC) algorithm, also known as the particle filter. Nevertheless, this method tends to be inefficient when applied to high-dimensional problems. In this chapter, we present, an...

The estimation of time-varying aerosol size distributions on the basis of differential mobility particle sizer measurements is a dynamical inverse problem with a non-linear/non-Gaussian state space model. A sequential Monte Carlo approach for determining approximations for the state estimates is proposed. The vapour pressure, which is difficult to measure accurately, is here taken as an unknown...

A "realistic" interpolation or extrapolation of bathymetry, i.e., one that honors both the statistical character and the deterministic constraints of the data, is referred to as a "conditional simulation." The first step in generating a conditional simulation is derivation of a statistical model for bathymetry. We typically employ the anisotropic von Kármán model (Goff and Jordan, 1988), which ...

This work is a study of multivariate simulations of pollutants to assess the sampling uncertainty for the risk analysis of a contaminated site. The study started from data collected for a remediation project of a steelworks in northern Italy. The soil samples were taken from boreholes excavated a few years ago and analyzed by a chemical laboratory. The data set comprises concentrations of sever...

We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a thrifty sequential design algorithm, in terms of computational speed, compared to the established MCMC alternative. The latter is less ideal for sequential design since it must be restarted and iterated to convergence with...

Sequential Bayesian estimation for nonlinear dynamic state-space models involves recursive estimation of filtering and predictive distributions of unobserved time varying signals based on noisy observations. This paper introduces a new filter called the Gaussian particle filter1. It is based on the particle filtering concept, and it approximates the posterior distributions by single Gaussians, ...

We introduce a new sequential algorithm for making robust predictions in the presence of changepoints. Unlike previous approaches, which focus on the problem of detecting and locating changepoints, our algorithm focuses on the problem of making predictions even when such changes might be present. We introduce nonstationary covariance functions to be used in Gaussian process prediction that mode...