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This thesis examines the processes of the formation and re-formation of folk cultures in north-eastern Scotland. Focusing on the area surrounding Aberdeen, I am interested in the historical and contemporary relationships between Highland and north-eastern versions of Scottishness. During the 1990's, a burgeoning north-eastern regional and cultural self-awareness, centred on the "Doric" dialect, was evidenced in the founding of the Elphinstone Research Institute at the University of Aberdeen, and in the founding of an annual Doric Festival. This institutionalisation has taken place in a situation in which Scottish national traditions have remained popular in the region and the Scottish nation has experienced a greater degree of political autonomy. The continued reproduction of both Scottishness and north-easternness is resultant of the ways in which culture and language have been mobilised, politicised and legitimised through the erection and operation of institutional frameworks. The role of expert and enthusiast knowledges in the propagation of cultural trends is analysed here through an exploration of the spectacularisation and banalisation of tradition. In particular, I unpack the tensions that claims for authenticity create with regard to the ballad and literary traditions of the north-east, and to the Highland Games and Gatherings of the same region. Exploring joint actions and knowledges, I demonstrate the efficacy of performative language and embodied practice in communicating, stabilising and normalising rhetorical ideas concerning Scottish cultures. As the contingent factors that give language and practice variable meanings become fixed through citation, the formation of dominant readings is enabled. The normalisation of spectacular events is crucial in the communication of authenticity and the establishment and recreation of national or regional identities. As perceived authenticity substitutes for objectivity in the popular imagination, everyday apprehensions of Scottishness and north- easternness are both made more durable and infused with a strong legitimacy by their very performance and naming.

This dissertation focuses on prediction and inference problems for complex spatiotemporal systems. I explore three specific problems in this area---motivated by real data examples---and discuss the theoretical motivations for the proposed methodology, implementation details, and inference/performance on data of interest. Chapter 1 introduces a novel time series model that improves the accuracy of lung tumor tracking for radiotherapy. Tumor tracking requires real-time, multiple-step ahead forecasting of a quasi-periodic time series recording instantaneous tumor locations. Our proposed model is a location-mixture autoregressive (LMAR) process that admits multimodal conditional distributions, fast approximate inference using the EM algorithm and accurate multiple-step ahead predictive distributions. Compared with other families of mixture autoregressive models, LMAR is easier to fit (with a smaller parameter space) and better suited to online inference and multiple-step ahead forecasting as there is no need for Monte Carlo. Against other candidate models in statistics and machine learning, our model provides superior predictive performance for clinical data. Chapter 2 develops a stochastic process model for the spatiotemporal evolution of a basketball possession based on tracking data that records each player's exact location at 25Hz. Our model comprises of multiresolution transition kernels that simultaneously describe players' continuous motion dynamics along with their decisions, ball movements, and other discrete actions. Many such actions occur very sparsely in player $\times$ location space, so we use hierarchical models to share information across different players in the league and disjoint regions on the basketball court---a challenging problem given the scale of our data (over 400 players and 1 billion space-time observations) and the computational cost of inferential methods in spatial statistics. Our framework, in addition to offering valuable insight into individual players’ behavior and decision-making, allows us to estimate the instantaneous expected point value of an NBA possession by averaging over all possible future possession paths. In Chapter 3, we investigate Gaussian process regression where inputs are subject to measurement error. For instance, in spatial statistics, input measurement errors occur when the geographical locations of observed data are not known exactly. Such sources of error are not special cases of ``nugget'' or microscale variation, and require alternative methods for both interpolation and parameter estimation. We discuss some theory for Kriging in this regime, as well as using Hybrid Monte Carlo to provide predictive distributions (and parameter estimates, if necessary). Through simulation study and analysis of northern hemipshere temperature data from the summer of 2011, we show that appropriate methods for incorporating location measurement error are essential to reliable inference in this regime., Statistics