Books by Tshilidzi Marwala

What happened to the 'old' intellectual movement? What happened to the thinkers who inspired and led our struggle? In pursuit of answers to these questions, MISTRA in partnership with the Liliesleaf Trust, hosted a roundtable in March 2015 on the theme 'The Role of Intellectuals in the State-Society Nexus'.

This self-contained book, written by active researchers, presents up-to-date information on smart maintenance strategies for human-robot interaction (HRI) and the associated applications of novel search algorithms in a single volume, eliminating the need to consult scattered resources.

The issue of missing data imputation has been extensively explored in information engineering. This book presents methods and technologies in estimation of missing values given the observed data. It covers techniques such as radial basis functions, support vector machines, and principal component analysis.

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical EngineeringTshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South AfricaSondipon Adhikari, Swansea University, UKCovers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineeringFinite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering.The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering.Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering.Key features:* Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.* Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations.The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.