This paper is concerned with making Bayesian inference from data that are assumed to be drawn from a Bingham distribution. A barrier to the Bayesian approach is the parameterdependent normalising constant of the Bingham distribution, which, even when it can be evaluated or accurately approximated, would have to be calculated at each iteration of an MCMC scheme, thereby greatly increasing the computational burden. We propose a method which enables exact (in Monte Carlo sense) Bayesian inference for the unknown parameters of the Bingham distribution by completely avoiding the need to evaluate this constant. We apply the method to simulated and real data, and illustrate that it is simpler to implement, faster, and performs better than an alternative algorithm that has recently been proposed in the literature.

We detail an approach to develop Stein's method for bounding integral metrics on probability measures defined on a Riemannian manifold M . Our approach exploits the relationship between the generator of a diffusion on M with target invariant measure and its characterising Stein operator. We consider a pair of such diffusions with different starting points, and investigate properties of solution to the Stein equation based on analysis of the distance process between the pair. Several examples elucidating the role of geometry of M in these developments are presented.

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed of...

In the present study, we performed a cross-sectional survey to determine the occurrence and genotype distribution of T. gondii DNA in soil samples collected from different sources from six geographic regions in China. Between March 2015 and June 2017, 2100 soil samples were collected from schools, parks, farms and coastal beaches, and examined for T. gondii DNA using three PCR assays targeting 529-bp repeat element (RE) sequence, B1 gene and ITS-1 gene sequences. Also, we investigated whether geographic region, soil source and type, and sampling season can influence the prevalence of T. gondii DNA in the soil. Soil samples collected from farms and parks had the highest prevalence, whereas samples collected from school playgrounds and coastal beaches had the lowest prevalence. PCR assays targeting 529bp RE and ITS-1 gene sequences were more sensitive than the B1 gene-based assay. Positive PCR products were genotyped using multi-locus PCR-RFLP, and ToxoDB #9 was the predominant genotype found in the contaminated soil samples. Multiple logistic regression identified factors correlated significantly with the presence of T. gondii DNA in the soil to be the source of the soil, including farms (odds ratio 3.10; 95% confidence interval [CI], 1.52 to 6.29; p = 0.002) and parks (2.59; 95% CI 1.28 to 5.27; p = 0.009). These results show that Chinese soil hosts T. gondii of the most prevalent genotype in China (ToxoDB#9) and that the soil type influences infection patterns.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straight-forward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components.