Numerical analysis of distribution patterns in the British and European floras

Imperial Users onl

3rd Workshop in Symbolic Data Analysis: book of abstracts

This workshop is the third regular meeting of researchers interested in Symbolic Data Analysis. The main aim of the event is to favor the meeting of people and the exchange of ideas from different fields - Mathematics, Statistics, Computer Science, Engineering, Economics, among others - that contribute to Symbolic Data Analysis

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Doctor of Philosophy

dissertationScale-bridging models are created to capture desired characteristics of high-fidelity models within low-fidelity model-forms for the purpose of allowing models to function at required spacial and/or temporal scales. The development, analysis, and application of scale-bridging models will be the focus of this dissertation. The applications dictating scales herein are large-scale computational fluid dynamics codes. Three unique scale-bridging models will be presented. First, the development and validation of a multiple-polymorph, particle precipitation modeling framework for highly supersaturated CaCO3 systems will be presented. This precipitation framework is validated against literature data, as well as explored for additional avenues of validation and potential future applications. Following this will be an introduction to the concepts of validation and uncertainty quantification and an approach for credible simulation development based upon those concepts. The credible simulation development approach is demonstrated through a spring-mass-damper pedagogical example. Bayesian statistical methods are commonly applied to validation and uncertainty quantification issues and the well-known Kennedy O'Hagan approach towards model-form uncertainty will be explored thoroughly using a chemical kinetics pedagogical example. Additional issues and ideas surrounding model-form uncertainty such as the identification problem will also be considered. Bayesian methods will then be applied towards the creation of a scale-bridging model for coal particle heat capacity and enthalpy modeling. Lastly, an alternative validation and uncertainty quantification technique, known as consistency testing, will be utilized to create a scale-bridging model for coal particle devolatilization. The credibility of the devolatilization scale-bridging model due to the model development process is assessed and found to have benefited from the use of validation and uncertainty quantification practices

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Sequence analysis: its past, present, and future

This article marks the occasion of Social Science Research’s 50th anniversary by reflecting on the progress of sequence analysis (SA) since its introduction into the social sciences four decades ago, with focuses on the developments of SA thus far in the social sciences and on its potential future directions. The application of SA in the social sciences, especially in life course research, has mushroomed in the last decade and a half. Using a life course analogy, we examined the birth of SA in the social sciences and its childhood (the first wave), its adolescence and young adulthood (the second wave), and its future mature adulthood in the paper. The paper provides a summary of (1) the important SA research and the historical contexts in which SA was developed by Andrew Abbott, (2) a thorough review of the many methodological developments in visualization, complexity measures, dissimilarity measures, group analysis of dissimilarities, cluster analysis of dissimilarities, multidomain/multichannel SA, dyadic/polyadic SA, Markov chain SA, sequence life course analysis, sequence network analysis, SA in other social science research, and software for SA, and (3) reflections on some future directions of SA including how SA can benefit and inform theory-making in the social sciences, the methods currently being developed, and some remaining challenges facing SA for which we do not yet have any solutions. It is our hope that the reader will take up the challenges and help us improve and grow SA into maturity