Aquiet revolution has been going on in town planning and architectural circles over the last decade. Established zoning and urban design ideas are being questioned and reversed. Urban planners and designers are reviving and reinterpreting ideas that prevailed before the automobile spread our cities apart and hollowed out their cores, before mass transit became the transportation mode for an und...

“The Virtual Metropolis” is an interactive installation combining traditional ink drawings and stop motion animation with video game technology. The artwork features various intermingled imaginary environments including cityscapes, factory districts and war zones. The user is invited to explore these worlds, seamlessly moving from one area to next, to discover satirically iconic animations and ...

We propose an adaptive independent Metropolis–Hastings algorithm with the ability to learn from all previous proposals in the chain except the current location. It is an extension of the independent Metropolis–Hastings algorithm. Convergence is proved provided a strong Doeblin condition is satisfied, which essentially requires that all the proposal functions have uniformly heavier tails than th...

We describe a serial algorithm called feature-inclusion stochastic search, or FINCS, that uses online estimates of edge-inclusion probabilities to inform the process of Bayesian model determination in Gaussian graphical models. FINCS is compared to Metropolis-based search methods and found to be superior along a variety of dimensions, leading to more accurate and less volatile estimates of edge...

Classifiers based on Bayesian networks are usually learned with a fixed structure or a small subset of possible structures. In the presence of unlabeled data this strategy can be detrimental to classification performance, when the assumed classifier structure is incorrect. In this paper we present a classification driven learning method for Bayesian network classifiers that is based on Metropol...

In this paper we present a Bayesian analysis of location-scale regression models assuming standard lifetime distributions and an additional error term with a mixture of normal distributions. Considering a censored lifetime data set, we use Gibbs sampling with Metropolis-Hastings algorithm to get Bayesian quantities of interest. The proposed regression model gives a great flexibility to fit life...

The stability and ergodicity properties of an adaptive random walk Metropolis algorithm are considered. The algorithm adjusts the scale of the symmetric proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of this algorithm, the adapted scaling parameter is not constrained within a predefined compact interval. This makes the algor...