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...

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...

| This paper presents a geometric approach to estimating subspaces as elements of complex Grassmann-manifold, with each subspace represented by its unique, complex projection matrix. Variation between the sub-spaces is modeled by rotating their projection matrices via the action of unitary matrices (elements of the unitary group (U(n))). Subspace estimation or tracking then corresponds to the i...

The Metropolis algorithm [8] is a mainstay of scientific computing. Indeed it appears first on a list of the “Top Ten Algorithms” [12]. It gives a method for sampling from probability distributions on high-dimensional spaces when these distributions are only known up to a normalizing constant. For background and references to extensive applications in physics, chemistry, biology and statistics,...

This paper is concerned with the online public engagement ‘Leitbild Metropolis Hamburg – Growing City’ which has been conducted in the context of the EU project DEMOS (Delphi Mediation Online System). The result of DEMOS is an innovative Internet platform facilitating democratic discussions and participative public opinion formation. The test of the DEMOS approach and the software system during...

A particular Markov chain Monte Carlo algorithmis constructed to allow Bayesian inference in a hidden Markov model used in hematology. The algorithm has an outer Gibbsian structure, and incorporates both Metropolis and Hastings updates to move through the space of possible hidden states. While somewhat sophisticated, this algorithm still has problems getting around the infinite-dimensional spac...

This paper examines the constraints to physical planning in Kano Metropolis. Kano been one of the most populous cities in Nigeria, is undergoing tremendous urban expansion particularly at the edge of the city. This expansion could be attributed to not only population growth but also redistribution of activities within the metropolitan area. Rapid population growth has increased the tendency of ...

that is, the sample covariance matrix of the history of the chain plus a (small) constant ǫ > 0 multiple of the identity matrix I. The lower bound on the eigenvalues of Sn induced by the factor ǫI is theoretically convenient, but practically cumbersome, as a good value for the parameter ǫ may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitl...