On block updating in Markov random field models for disease mapping. (REVISED, May 2001)

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely bad due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different models: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. We apply the proposed algorithms to two datasets known from the literature. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. In certain situations, even updating of all or nearly all parameters in one block may be necessary. Implementation of such block algorithms is surprisingly easy using methods for fast sampling of Gaussian Markov random fields (Rue, 2000). By comparison, estimates of the relative risk and related quantities, such as the posterior probability of an exceedence relative risk, based on single-site updating, can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components

Unearthing Charles Rolwing : the problem of documentation in the small museum.

This Curatorial project examines issues that museums face in the documentation of permanent collections, using the Kentucky Museum of Art and Craft (KMAC) and its piece Angel, by Charles Rolwing, as specific examples. The project focuses on museum accession policy, with discussion of works from smaller collections, similar to KMAC, and from larger museums. Specifically, objects are accepted into the collection through acquisition or accession, the former referring to all donated objects accepted by the museum and the latter referring to objects accepted into the permanent collection. A proposed collection policy for KMAC is discussed, including the procedure by which objects are accepted into the collection, and the different ways KMAC could determine its policy for the future. The discussion of accession policy leads to my personal involvement in KMAC and the organization of the permanent collection. The catalogue and accession numbering process implemented by many museums is discussed, demonstrating how these standards can be incorporated in the proposed catalogue for KMAC. Lastly, Charles Rolwing’s life as an artist is explored, illuminating the importance of thorough research before fully accessioning objects into the permanent collection of the museum

Modelling seasonal patterns in longitudinal profiles with correlated circular random walks

Seasonal patterns, as they occur in time series of infectious disease surveillance counts, are frequently modelled using a superposition of sine and cosine functions. However, in some cases this might be too simple. We propose the use of circular second order random walks instead and extend this approach to multivariate time series of counts. A correlated Gaussian Markov random field approach combines a uniform correlation matrix with a circular random walk to allow the seasonal pattern to be similar across regions, say, but not identical. Thus, spatially-varying disease onsets may be accounted for. The methodology is applied to weekly number of deaths from in uenza and pneumonia in nine major regions of the USA

A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

International audienceA method for assimilating remotely sensed snow covered area (SCA) into the snow subroutine of a grid distributed precipitation-runoff model (PRM) is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC), which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E), based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started and the snow coverage is close to unity. Caution is therefore required when using early images

Photo-induced reduction of graphene oxide coating on optical waveguide and consequent optical intermodulation

Increased absorption of transverse-magnetic (TM) - polarised light by a graphene-oxide (GO) coated polymer waveguide has been observed in the presence of transverse-electric (TE) - polarised light. The GO-coated waveguide exhibits very strong photo-absorption of TE-polarised light - and acts as a TM-pass waveguide polariser. The absorbed TE-polarised light causes a significant temperature increase in the GO film and induces thermal reduction of the GO, resulting in an increase in optical-frequency conductivity and consequently increased optical propagation loss. This behaviour in a GO-coated waveguide gives the action of an inverted optical switch/modulator. By varying the incident TE-polarised light power, a maximum modulation efficiency of 72% was measured, with application of an incident optical power level of 57 mW. The GO-coated waveguide was able to respond clearly to modulated TE-polarised light with a pulse duration of as little as 100 μs. In addition, no wavelength dependence was observed in the response of either the modulation (TE-polarised light) or the signal (TM-polarised light)

Correlated GMRF priors for multivariate age-period-cohort models

Multivariate age-period-cohort models have recently been proposed for the analysis of heterogeneous time trends. For a fully Bayesian analysis, Gaussian Markov random field (GMRF) priors are typically used. However, standard GMRF priors do not account for a potential dependence between outcomes. We present an extended approach based on correlated smoothing priors and corre-lated overdispersion parameters. Algorithmic routines are based on either Markov chain Monte Carlo or integrated nested Laplace approximations. Results are discussed for data on female mortality in Denmark and Norway and compared by means of DIC, proper scoring rules and the marginal likelihood