As the Catholic Church continues to expand in Uganda, this thesis offers an ethnographic study of engagement with Catholicism among the laity in a relatively new, rural parish in the Teso Region of eastern Uganda. Founded in the late 1990s, the creation of a new parish in the Sub-County of Buluya has brought people into closer proximity to the Catholic Church, its priests, and its doctrines, throwing into sharp relief some of the tensions between Catholic and local moral and spiritual frameworks. Based on 17 months of ethnographic and archival fieldwork, I examine the way in which people negotiate the challenges posed by this change, as they seek to balance the need to use the tools Catholicism offers for getting on in post-colonial Uganda with desires to protect older ways of seeing the world and acting in it. My central argument is that people respond to the Church’s attempts to embed itself as an all-encompassing presence and influence in the lives of its members, by engaging in processes of limiting this presence and influence. By remoulding and realigning some of its central concepts, by resisting wholeheartedly committing to its claims to spiritual knowledge and healing potential, and by isolating its moral and behavioural directives from certain aspects of their lives, the laity in Buluya rein in the Catholic Church’s attempts to permeate and dominate all aspects of their lives. I suggest that these limits go hand in hand with the pervasive religious uncertainty that underpins people’s engagement with the Church, arguing that these limiting practices serve to maintain their religious uncertainty as doors are left open to alternative ways of engaging with their social and spiritual surroundings. In turn, the productive potential of this religious uncertainty encourages these limits to be enacted and maintained. Limiting Catholicism, in essence, enables people in Buluya to commit to it.

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The goal of top-k ranking is to rank individuals so that the best k of them can be determined. Depending on the application domain, an individual can be a person, a product, an event, or just a collection of data or information for which an ordering makes sense. In the context of databases, top-k ranking has been studied in two distinct directions, depending on whether the stored information is certain or uncertain. In the former, the past research has focused on efficient query processing. In the latter case, a number of semantics based on possible worlds have been proposed and computational mechanisms investigated for what are called uncertain databases or probabilistic databases, where a tuple is associated with a membership probability indicating the level of confidence on the stored information. In this thesis, we study top-k ranking with uncertain data in two general areas. The first is on pruning for the computation of top-k tuples in a probabilistic database. We investigate the theoretical basis and practical means of pruning for the recently proposed, unifying framework based on parameterized ranking functions. As such, our results are applicable to a wide range of ranking functions. We show experimentally that pruning can generate orders of magnitude performance gains. In the second area of our investigation, we study the problem of top-k ranking for objects with multiple attributes whose values are modeled by probability distributions and constraints. We formulate a theory of top-k ranking for objects by a characterization of what constitutes the strength of an object, and show that a number of previous proposals for top-k ranking are special cases of our theory. We carry out a limited study on computation of top-k objects under our theory. We reveal the close connection between top-k ranking in this context and high-dimensional space studied in mathematics, in particular, the problem of computing the volumes of high-dimensional polyhedra expressed by linear inequations is a special case of top-k ranking of objects, and as such, the algorithms formulated for the former can be employed for the latter under the same conditions.

Finding and developing oil and gas resources requires accurate geological information with which to formulate strategies for exploration and exploitation ventures. When data are scarce, statistical procedures are sometimes substituted to compensate for the lack of information about reservoir properties. The most modern methods incorporate geostatistics. Even the best geostatistical methods yield results with varying degrees of uncertainty in their solutions. Geological information is, by its nature, spatially limited and the geoscientist is handicapped in determining appropriate values for various geological parameters that affect the final reservoir model (Massonnat, 1999). This study focuses on reservoir models that depend on geostatistical methods. This is accomplished by quantifying the uncertainty in outcome of reservoir simulations as six different geological variables are changed during a succession of reservoir simulations. In this study, variations in total fluid produced are examined by numerical modeling. Causes of uncertainty in outcomes of the model runs are examined by changing one of six geological parameters for each run. The six geological parameters tested for their impact on reservoir performances include the following: 1) variogram range used to krig thickness layers, 2) morphology around well 14, 3) shelf edge orientation, 4) bathymetry ranges attributed for each facies, 5) variogram range used to simulate facies distribution, 6) extension of the erosion at top of the reservoir. The parameters were assigned values that varied from a minimum to a maximum quantity, determined from petrophysical and core analysis. After simulation runs had been completed, a realistic, 3-dimensional reservoir model was developed that revealed a range of reservoir production data. The parameters that had the most impact on reservoir performance were: 1) the amount of rock eroded at the top of the reservoir zone and 2) the bathymetry assigned to the reservoir facies. This study demonstrates how interaction between geological parameters influence reservoir fluid production, how variations in those parameters influence uncertainties in reservoir simulations, and it highlights the interdependencies between geological variables. The analysis of variance method used to quantify uncertainty in this study was found to be rapid, accurate, and highly satisfactory for this type of study. It is recommended for future applications in the petroleum industry.

This study proposes a new, easily applied method to quantify uncertainty in production forecasts for a volumetric gas reservoir based on a material balance model (p/z vs. Gp). The new method uses only observed data and mismatches between regression values and observed values to identify the most probable value of gas reserves. The method also provides the range of probability of values of reserves from the minimum to the maximum likely value. The method is applicable even when only limited information is available from a field. Previous methods suggested in the literature require more information than our new method. Quantifying uncertainty in reserves estimation is becoming increasingly important in the petroleum industry. Many current investment opportunities in reservoir development require large investments, many in harsh exploration environments, with intensive technology requirements and possibly marginal investment indicators. Our method of quantifying uncertainty uses a priori information, which could come from different sources, typically from geological data, used to build a static or prior reservoir model. Additionally, we propose a method to determine the uncertainty in our reserves estimate at any stage in the life of the reservoir for which pressure-production data are available. We applied our method to San Juan reservoir at Santa Rosa Field, Venezuela. This field was ideal for this study because it is a volumetric reservoir for which the material balance method, the p/z vs. Gp plot, appears to be appropriate.

The classical mean-variance model, proposed by Harry Markowitz in 1952, has been one of the most powerful tools in the field of portfolio optimization. In this model, parameters are estimated by their sample counterparts. However, this leads to estimation risk, which the model completely ignores. In addition, the mean-variance model fails to incorporate behavioral aspects of investment decisions. To remedy the problem, the notion of ambiguity aversion has been addressed by several papers where investors acknowledge uncertainty in the estimation of mean returns. We extend the idea to the variances and correlation coefficient of the portfolio, and study their impact. The performance of the portfolio is measured in terms of its Sharpe ratio. We consider different cases where one parameter is assumed to be perfectly estimated by the sample counterpart whereas the other parameters introduce ambiguity, and vice versa, and investigate which parameter has what impact on the performance of the portfolio.

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This dissertation consists of two parts with two different topics. In the first part, we investigate ``Load-Share Model" for modeling dependency among components in a multi-component system. Systems, where the components share the total applied load, are often referred to as load sharing systems. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example (see Kvam and Pena (2002)). When it comes to load-share model, the most interesting component is the underlying principle that dictates how failure rates of surviving components change after some components in the system fail. This kind of principle depends mostly on the reliability application and how the components within the system interact through the reliability structure function. Until now, research involving load-share models have emphasized the characterization of system reliability under a known load-share rule. Methods for reliability analysis based on unknown load-share rules have not been fully developed. So, in the first part of this dissertation, 1) we model the dependence between system components through a load-share framework, with the load-sharing rule containing unknown parameters and 2) we derive methods for statistical inference on unknown load-share parameters based on maximum likelihood estimation. In the second half of this thesis, we extend the existing uncertain supply literature to a case where the supply uncertainty dwells in the logistics operations. Of primary interest in this study is to determine the optimal order amount for the retailer given uncertainty in the supply-chain's logistics network due to unforeseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplaced products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events.

Since Hubbert proposed the "peak oil" concept to forecast ultimate recovery of crude oil for the U.S. and the world, there have been countless debates over the timing of peak world conventional oil production rate and ultimate recovery. From review of the literature, forecasts were grouped into those that are like Hubbert's with an imminent peak, and those that do not predict an imminent peak. Both groups have bases for their positions. Viewpoints from the two groups are polarized and the rhetoric is pointed and sometimes personal. A big reason for the large divide between the two groups is the failure of both to acknowledge the significant uncertainty in their estimates. Although some authors attempt to quantify uncertainty, most use deterministic methods and present single values, with no ranges. This research proposes that those that do attempt to quantify uncertainty underestimate it significantly. The objective of this thesis is to rigorously quantify the uncertainty in estimates of ultimate world conventional oil production and time to peak rate. Two different methodologies are used. The first is a regression technique based on historical production data using Hubbert's model and the other methodology uses mathematical models. However, I conduct the analysis probabilistically, considering errors in both the data and the model, which results in likelihood probability distributions for world conventional oil production and time to peak rate. In the second method, I use a multiple-experts analysis to combine estimates from the multitude of papers presented in the literature, yielding an overall distribution of estimated world conventional oil production. Giving due consideration to uncertainty, Hubbert-type mathematical modeling results in large uncertainty ranges that encompass both groups of forecasts (imminent peak and no imminent peak). These ranges are consistent with those from the multiple-experts analysis. In short, the industry does not have enough information at this time to say with any reliability what the ultimate world conventional oil production will be. It could peak soon, somewhere in the distant future, or somewhere in between. It would be wise to consider all of these possible outcomes in planning and making decisions regarding capital investment and formulation of energy policy.