Revised age estimate of the Mjauvotn tephra A on the Faroe Islands based on Bayesian modelling of C-14 dates from two lake sequences

Tephra horizons are potentially perfect time markers for dating and cross-correlation among diverse Holocene palaeoenvironmental records such as ice cores and marine and terrestrial sequences, but we need to trust their age. Here we present a new age estimate of the Holocene Mjauvotn tephra A using accelerator mass spectrometry C-14 dates from two lakes on the Faroe Islands. With Bayesian age modelling it is dated to 6668-6533 cal. a BP (68.2% confidence interval) - significantly older and better constrained than the previous age. Copyright (C) 2010 John Wiley & Sons, Ltd.

Precision dating of the Palaeolithic: A new radiocarbon chronology for the Abri Pataud (France), a key Aurignacian sequence

This paper presents a new series of AMS dates on ultrafiltered bone gelatin extracted from identified cutmarked or humanly-modified bones and teeth from the site of Abri Pataud, in the French Dordogne. The sequence of 32 new determinations provides a coherent and reliable chronology from the site's early Upper Palaeolithic levels 5-14, excavated by Hallam Movius. The results show that there were some problems with the previous series of dates, with many underestimating the real age. The new results, when calibrated and modelled using a Bayesian statistical method, allow detailed understanding of the pace of cultural changes within the Aurignacian I and II levels of the site, something not achievable before. In the future, the sequence of dates will allow wider comparison to similarly dated contexts elsewhere in Europe. High precision dating is only possible by using large suites of AMS dates from humanly-modified material within well understood archaeological sequences modelled using a Bayesian statistical method. © 2011.

Chronology of the site of Grotte du Renne, Arcy-sur-Cure, France: implications for Neanderthal symbolic behaviour

Higham et al (2010) published a large series of new dates from the key French Palaeolithic site of the Grotte du Renne at Arcy-sur-Cure. The site is important because it is one of only two sites in Europe in which Châtelperronian lithic remains co-occur with Neanderthal human remains. A large series of dates from the Mousterian, Châtelperronian, Aurignacian and Gravettian levels of the site was obtained. The 14C results showed great variability, which Higham et al (2010) interpreted as most likely to be due to mixing of archaeological material in the site. In contrast, Caron et al (2011) suggested that the site stratigraphy is well preserved and that the problem with the variability in the radiocarbon ages was due to unremoved contamination in the dated bone. In this paper we address their critique of the original Higham et al (2010) paper

Modelling Patient Duration of Stay to Facilitate Resource Management of Geriatric Hospitals

A fundamental aspect of health care management is the effective allocation of resources. This is of particular importance in geriatric hospitals where elderly patients tend to have more complex needs. Hospital managers would benefit immensely if they had advance knowledge of patient duration of stay in hospital. Managers could assess the costs of patient care and make allowances for these in their budget. In this paper, we tackle this important problem via a model which predicts the duration of stay distribution of patients in hospital. The approach uses phase-type distributions conditioned on a Bayesian belief network.

Modelling attribute non-attendance in choice experiments for rural landscape valuation

Non-market effects of agriculture are often estimated using discrete choice models from stated preference surveys. In this context we propose two ways of modelling attribute non-attendance. The first involves constraining coefficients to zero in a latent class framework, whereas the second is based on stochastic attribute selection and grounded in Bayesian estimation. Their implications are explored in the context of a stated preference survey designed to value landscapes in Ireland. Taking account of attribute non-attendance with these data improves fit and tends to involve two attributes one of which is likely to be cost, thereby leading to substantive changes in derived welfare estimates.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

A compendium of modelling techniques

This Integration Insight provides a brief overview of the most popular modelling techniques used to analyse complex real-world problems, as well as some less popular but highly relevant techniques. The modelling methods are divided into three categories, with each encompassing a number of methods, as follows: 1) Qualitative Aggregate Models (Soft Systems Methodology, Concept Maps and Mind Mapping, Scenario Planning, Causal (Loop) Diagrams), 2) Quantitative Aggregate Models (Function fitting and Regression, Bayesian Nets, System of differential equations / Dynamical systems, System Dynamics, Evolutionary Algorithms) and 3) Individual Oriented Models (Cellular Automata, Microsimulation, Agent Based Models, Discrete Event Simulation, Social Network
Analysis). Each technique is broadly described with example uses, key attributes and reference material.