Transportation is a necessity if the day-to-day economic activities of the society must move on. There are different modes of informal transport available in Nigeria. This study evaluated the determinants of the modal choice of informal transport among commuters in North central city of Ilorin, Nigeria. The study used primary data generated through a structured questionnaire administered to 100...

The Hastings-Metropolis algorithm is a general MCMC method for sampling from a density known up to a constant. Geometric convergence of this algorithm has been proved under conditions relative to the instrumental distribution (or proposal). We present an inhomogeneous Hastings-Metropolis algorithm for which the proposal density approximates the target density, as the number of iterations increa...

This article contains an overview of the literature concerning the computational complexity of Metropolis-Hastings based MCMC methods for sampling probability measures on Rd , when the dimension d is large. The material is structured in three parts addressing, in turn, the following questions: (i) what are sensible assumptions to make on the family of probability measures indexed by d ?; (ii) w...

We present Metropolis Instant Radiosity (MIR), an unbiased algorithm to solve the Light Transport problem. MIR is a hybrid technique which consists in representing the incoming radiance field by a set of Virtual Point Lights (VPLs) and in computing the response of all sensors in the scene (i.e. camera captors) by accumulating their contributions. In contrast to other similar approaches, we prop...

CPU Modeling and Refinement in Metropolis

Background The emergence of a pandemic presents challenges and opportunities for healthcare, health promotion interventions, overall improvement in healthcare seeking behaviour. This study explored the impact COVID-19 on knowledge, lifestyle, behaviour among residents resource-limited setting Ghana. Methods qualitative adopted an exploratory design to collect data from 20 adult Cape Coast Metro...

In this paper, we show how to use Bayesian approach in the multiplicative heteroscedasticity model proposed by Harvey (1976), where the Gibbs sampler and the Metropolis-Hastings (MH) algorithm are applied. Some candidate-generating densities are considered in our Metropolis-Hastings algorithm. We carry out Monte Carlo study to examine the properties of the estimates via Bayesian approach and it...

This study focuses on the development of a web-based GIS for public healthcare system. The development of this system is motivated to provide opportunities for the healthcare workers to gain access to vital information that can aid him/her in the location of viable hospitals for the patients to fully enjoy available enhanced healthcare services. Currently, three major problems still exist in th...

A detailed computational study of compositional segregation during growth of colloidal binary solid-solution crystals is presented. Using a comprehensive set of Metropolis Monte Carlo simulations, we probe the influence of colloid size, interaction strength, and interaction range on the segregation process. The results are interpreted in terms of a simple, but descriptive mechanistic model that...

Importance sampling and Metropolis-Hastings sampling (of which Gibbs sampling is a special case) are two methods commonly used to sample multi-variate probability distributions (that is, Bayesian networks). Heretofore, the sampling of Bayesian networks has been done on a conventional “classical computer”. In this paper, we propose methods for doing importance sampling and Metropolis-Hastings sa...