Soil attributes, including those in mine spoil heaps, critically affect plant growth during land rehabilitation. Their characterization through a limited number of samples requires quantification of spatial variability, which is then used at various stages throughout the rehabilitation process and assists risk analysis and rehabilitation decision making. Stochastic simulation is a tool used for...

We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example complex simulator-based statistical models are fitted to data, and synthetic likelihood (SL) method is used form the estimates using computationally costly forward simulations. frame task as sequential experimental design problem, where function modelled with hi...

Maximum likelihood decoding algorithms for Gaussian MIMO linear channels are considered. Linearity over the field of real numbers facilitates the design of maximum likelihood decoders using number theoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorit...

In this thesis, we study the performance of the maximum-likelihood soft-decision sequential decoding algorithm (MLSDA) for convolutional codes, which is previously proposed in [1]. Instead of using the conventional Fano metric, the proposed algorithm employs a new metric based on a variation of the Wagner rule, which is referred to as the second form of the maximum-likelihood decoding rule. The...

The recent emergence of the discrete fractional Fourier transform has spurred research activity aiming at generating Hermite-Gaussian-like (HGL) orthonormal eigenvectors of the discrete Fourier transform (DFT) matrix F. By exploiting the unitarity of matrix F – resulting in the orthogonality of its eigenspaces pertaining to the distinct eigenvalues – the problem decouples into finding orthonorm...

The dynamical systems are comprised of two components that change over time: the state space and observation models. This study examines parameter inference in from perspective Bayesian inference. Inference on unknown parameters nonlinear non-Gaussian is challenging because posterior densities corresponding to do not have traceable formulations. Such a system represented by Ricker model, which ...

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...