Presented here are the results of a Metropolis–Hastings Markov chain Monte Carlo routine applied to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. The Metropolis– Hastings routine is described in detail, and examples show that signals may be detected and analysed from within noisy data. Using the Bayesian framework of statistic...

We consider optimal temperature spacings for Metropolis-coupled Markov chain Monte Carlo (MCMCMC) and Simulated Tempering algorithms. We prove that, under certain conditions, it is optimal (in terms of maximising the expected squared jumping distance) to space the temperatures so that the proportion of temperature swaps which are accepted is approximately 0.234. This generalises related work by...

We propose that entity queries are generated via a two-step process: users first select entity facts that can distinguish target entities from the others; and then choose words to describe each selected fact. Based on this query generation paradigm, we propose a new entity representation model named as entity factoid hierarchy. An entity factoid hierarchy is a tree structure composed of factoid...

One of the most widely used samplers in practice is the component-wise MetropolisHastings (CMH) sampler that updates in turn the components of a vector spaces Markov chain using accept-reject moves generated from a proposal distribution. When the target distribution of a Markov chain is irregularly shaped, a ‘good’ proposal distribution for one part of the state space might be a ‘poor’ one for ...

Background and Aim: Climate change on the earth is changing faster than ever before in the history. Cities are highly vulnerable to this climate change. Therefore, the present study aimed to investigate climate change in the metropolis of Tehran during the period 1991-2020 and help understand the limitations that cities may have in confronting climate change. Materials and Methods: This descri...

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...

Prefetching is a simple and general method for single-chain parallelisation of the Metropolis-Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of time. Improved Metropolis-Hastings prefetching algorithms are presented and evaluated. It is shown how to use available information to make better predictions of the future states of the chain and increase the e¢ ...

The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. We describe the basic Laplace-Metropolis estimator for models without random eeects. For models wi...

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...

The case study documented in this tutorial exercises the capabilities of the Metropolis Design Environment with an industrial-sized design. As a typical design example in Metropolis, this case study consists of a functional network, an architectural network, and a mapping network. The functional network models a simple application where data is obtained from two independent sources, manipulated...