All entries for Tuesday 19 April 2016
April 19, 2016
Barnes, C. (2015). Statistics in Anglo-Saxon Archaeology. MSc Dissertation. University of Warwick.
Statistical methods are increasingly finding applications in new disciplines; one area in which interest in such approaches has increased in recent years is archaeology, and in particular, in archaeological studies of Anglo-Saxon England. A question currently under consideration in this field is whether any evidence exists that buildings in settlements dated to the early Medieval period may have been planned according to a perpendicular grid system. Assessment of the features of a site of interest, and any spatial or angular patterns contained therein, has until now typically been carried out by visual appraisal of transcribed site plans. We propose a method by which a JPEG image of an archaeological site of interest may be converted into a set of points representing the locations of post-holes that once formed part of the support of large structures such as walls, buildings and fences. Defining the orientation of the post-holes by the direction in which its nearest neighbour lies, we are able to test objectively whether there is any evidence that larger features share a common orientation, and to assess whether a similar grid orientation is shared globally across multiple regions of the grid, or found only locally in smaller regions.
Writing about web page http://projecteuclid.org/euclid.aoas/1453993094
Zanella, G. (2016). Random Partition Models and Complementary Clustering of Anglo-Saxon Placenames. Annals of Applied Statistics, 9(4), 1792–1822. See http://projecteuclid.org/euclid.aoas/1453993094, also http://arxiv.org/abs/1409.6994.
Common cluster models for multi-type point processes model the aggregation of points of the same type. In complete contrast, in the study of Anglo-Saxon settlements it is hypothesized that administrative clusters involving complementary names tend to appear. We investigate the evidence for such a hypothesis by developing a Bayesian Random Partition Model based on clusters formed by points of different types (complementary clustering).
As a result, we obtain an intractable posterior distribution on the space of matchings contained in a -partite hypergraph. We apply the Metropolis–Hastings (MH) algorithm to sample from this posterior. We consider the problem of choosing an efficient MH proposal distribution and we obtain consistent mixing improvements compared to the choices found in the literature. Simulated Tempering techniques can be used to overcome multimodality and a multiple proposal scheme is developed to allow for parallel programming. Finally, we discuss results arising from the careful use of convergence diagnostic techniques.
This allows us to study a data set including locations and place-names of 1316 Anglo-Saxon settlements dated approximately around 750–850 AD. Without strong prior knowledge, the model allows for explicit estimation of the number of clusters, the average intra-cluster dispersion and the level of interaction among place-names. The results support the hypothesis of organization of settlements into administrative clusters based on complementary names.