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Modelling complex systems such as how the white rhinoceros Ceratotherium simum simum uses a landscape requires innovative and multi-disciplinary approaches. Bayesian networks have been shown to provide a dynamic, easily interpretable framework to represent real-world problems. This, together with advances in remote sensor technology to easily quantify environmental variables, make non-intrusive techniques for understanding and inference of ecological processes more viable than ever. However, when modelling an animal’s use of a landscape we only have access to presence locations. These data are also extremely susceptible to both temporal and spatial sampling bias in that animal presence locations often originate from aerial surveys or from individual rhinos fitted with tracking collars. In modelling species’ presence, little recognition is given to finding quantifiable drivers and managing confounding variables. Here we use presence-unlabelled modelling to construct Bayesian networks for rhino presence with remotely sensed covariates and show how it can provide an understanding of a complex system in a temporal and spatial context. We find that strategic unlabelled data sampling is important to counter sampling biases and discretisation of covariate data needs to be well considered in the tradeoff between computational efficiency and data accuracy. We show how learned Bayesian networks can be used to not only reveal interesting relations between drivers of rhino presence, but also to perform inference. Having temporally aware environmental variables such as soil moisture and distance to fire, allowed us to infer rhino presences for the following time step with incomplete evidence. We confirmed that in general, white rhinos tend to be close to surface water, rivers and previously burned areas with a preference for warm slopes. These relationships between drivers shift notably when modelling for individuals. We anticipate our dissertation to be a starting point for more sophisticated models of complex systems specifically investigating its use to model individual behaviour. / Dissertation (MEng)--University of Pretoria, 2020. / Industrial and Systems Engineering / MEng (Industrial Engineering) / Unrestricted

Includes bibliographical references (pages. 379-400). / This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity.

"Intelligent tutoring systems rely on student modeling to understand student behavior. The result of student modeling can provide assessment for student knowledge, estimation of student¡¯s current affective states (ie boredom, confusion, concentration, frustration, etc), prediction of student performance, and suggestion of the next tutoring steps. There are three focuses of this dissertation. The first focus is on better predicting student performance by adding more information, such as student identity and information about how many assistance students needed. The second focus is to analyze different performance and feature set for modeling student short-term knowledge and longer-term knowledge. The third focus is on improving the affect detectors by adding more features. In this dissertation I make contributions to the field of data mining as well as educational research. I demonstrate novel Bayesian networks for student modeling, and also compared them with each other. This work contributes to educational research by broadening the task of analyzing student knowledge to student knowledge retention, which is a much more important and interesting question for researchers to look at. Additionally, I showed a set of new useful features as well as how to effectively use these features in real models. For instance, in Chapter 5, I showed that the feature of the number of different days a students has worked on a skill is a more predictive feature for knowledge retention. These features themselves are not a contribution to data mining so much as they are to education research more broadly, which can used by other educational researchers or tutoring systems. "

Cancer is a multi-stage process resulting from accumulation of genetic mutations. Data obtained from assaying a tumor only contains the set of mutations in the tumor and lacks information about their temporal order. Learning the chronological order of the genetic mutations is an important step towards understanding the disease. The probability of introduction of a mutation to a tumor increases if certain mutations that promote it, already happened. Such dependencies induce what we call the monotonicity property in cancer progression. A realistic model of cancer progression should take this property into account. In this thesis, we present two models for cancer progression and algorithms for learning them. In the first model, we propose Progression Networks (PNs), which are a special class of Bayesian networks. In learning PNs the issue of monotonicity is taken into consideration. The problem of learning PNs is reduced to Mixed Integer Linear Programming (MILP), which is a NP-hard problem for which very good heuristics exist. We also developed a program, DiProg, for learning PNs. In the second model, the problem of noise in the biological experiments is addressed by introducing hidden variable. We call this model Hidden variable Oncogenetic Network (HON). In a HON, there are two variables assigned to each node, a hidden variable that represents the progression of cancer to the node and an observable random variable that represents the observation of the mutation corresponding to the node. We devised a structural Expectation Maximization (EM) algorithm for learning HONs. In the M-step of the structural EM algorithm, we need to perform a considerable number of inference tasks. Because exact inference is tractable only on Bayesian networks with bounded treewidth, we also developed an algorithm for learning bounded treewidth Bayesian networks by reducing the problem to a MILP. Our algorithms performed well on synthetic data. We also tested them on cytogenetic data from renal cell carcinoma. The learned progression networks from both algorithms are in agreement with the previously published results. MicroRNAs are short non-coding RNAs that are involved in post transcriptional regulation. A-to-I editing of microRNAs converts adenosine to inosine in the double stranded RNA. We developed a method for determining editing levels in mature microRNAs from the high-throughput RNA sequencing data from the mouse brain. Here, for the first time, we showed that the level of editing increases with development. / <p>QC 20130503</p>

Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a compact representation of a probabilistic problem, exploiting independence amongst variables that allows a factorization of the joint probability into much smaller local probability distributions.<p>The standard approach to probabilistic inference in Bayesian networks is to compile the graph into a join­tree, and perform computation over this secondary structure. While join­trees are among the most time­efficient methods of inference in Bayesian networks, they are not always appropriate for certain applications. The memory requirements of join­tree can be prohibitively large. The algorithms for computing over join­trees are large and involved, making them difficult to port to other systems or be understood by general programmers without Bayesian network expertise. <p>This thesis proposes a different method for probabilistic inference in Bayesian networks. We present a data structure called a conditioning graph, which is a run­time representation of Bayesian network inference. The structure mitigates many of the problems of join­tree inference. For example, conditioning graphs require much less space to store and compute over. The algorithm for calculating probabilities from a conditioning graph is small and basic, making it portable to virtually any architecture. And the details of Bayesian network inference are compiled away during the construction of the conditioning graph, leaving an intuitive structure that is easy to understand and implement without any Bayesian network expertise. <p>In addition to the conditioning graph architecture, we present several improvements to the model, that maintain its small and simplistic style while reducing the runtime required for computing over it. We present two heuristics for choosing variable orderings that result in shallower elimination trees, reducing the overall complexity of computing over conditioning graphs. We also demonstrate several compile and runtime extensions to the algorithm, that can produce substantial speedup to the algorithm while adding a small space constant to the implementation. We also show how to cache intermediate values in conditioning graphs during probabilistic computation, that allows conditioning graphs to perform at the same speed as standard methods by avoiding duplicate computation, at the price of more memory. The methods presented also conform to the basic style of the original algorithm. We demonstrate a novel technique for reducing the amount of required memory for caching. <p>We demonstrate empirically the compactness, portability, and ease of use of conditioning graphs. We also show that the optimizations of conditioning graphs allow competitive behaviour with standard methods in many circumstances, while still preserving its small and simple style. Finally, we show that the memory required under caching can be quite modest, meaning that conditioning graphs can be competitive with standard methods in terms of time, using a fraction of the memory.

Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a compact representation of a probabilistic problem, exploiting independence amongst variables that allows a factorization of the joint probability into much smaller local probability distributions.<p>The standard approach to probabilistic inference in Bayesian networks is to compile the graph into a join­tree, and perform computation over this secondary structure. While join­trees are among the most time­efficient methods of inference in Bayesian networks, they are not always appropriate for certain applications. The memory requirements of join­tree can be prohibitively large. The algorithms for computing over join­trees are large and involved, making them difficult to port to other systems or be understood by general programmers without Bayesian network expertise. <p>This thesis proposes a different method for probabilistic inference in Bayesian networks. We present a data structure called a conditioning graph, which is a run­time representation of Bayesian network inference. The structure mitigates many of the problems of join­tree inference. For example, conditioning graphs require much less space to store and compute over. The algorithm for calculating probabilities from a conditioning graph is small and basic, making it portable to virtually any architecture. And the details of Bayesian network inference are compiled away during the construction of the conditioning graph, leaving an intuitive structure that is easy to understand and implement without any Bayesian network expertise. <p>In addition to the conditioning graph architecture, we present several improvements to the model, that maintain its small and simplistic style while reducing the runtime required for computing over it. We present two heuristics for choosing variable orderings that result in shallower elimination trees, reducing the overall complexity of computing over conditioning graphs. We also demonstrate several compile and runtime extensions to the algorithm, that can produce substantial speedup to the algorithm while adding a small space constant to the implementation. We also show how to cache intermediate values in conditioning graphs during probabilistic computation, that allows conditioning graphs to perform at the same speed as standard methods by avoiding duplicate computation, at the price of more memory. The methods presented also conform to the basic style of the original algorithm. We demonstrate a novel technique for reducing the amount of required memory for caching. <p>We demonstrate empirically the compactness, portability, and ease of use of conditioning graphs. We also show that the optimizations of conditioning graphs allow competitive behaviour with standard methods in many circumstances, while still preserving its small and simple style. Finally, we show that the memory required under caching can be quite modest, meaning that conditioning graphs can be competitive with standard methods in terms of time, using a fraction of the memory.

A central problem in case based reasoning (CBR) is how to store and retrieve cases. One approach to this problem is to use exemplar based models, where only the prototypical cases are stored. However, the development of an exemplar based model (EBM) requires the solution of several problems: (i) how can a EBM be represented? (ii) given a new case, how can a suitable exemplar be retrieved? (iii) what makes a good exemplar? (iv) how can an EBM be learned incrementally? This thesis develops a new model, called a probabilistic exemplar based model, that addresses these research questions. The model utilizes Bayesian networks to develop a suitable representation and uses probability theory to develop the foundations of the developed model. A probability propagation method is used to retrieve exemplars when a new case is presented and for assessing the prototypicality of an exemplar. The model learns incrementally by revising the exemplars retained and by updating the conditional probabilities required by the Bayesian network. The problem of ignorance, encountered when only a few cases have been observed, is tackled by introducing the concept of a virtual exemplar to represent all the unseen cases. The model is implemented in C and evaluated on three datasets. It is also contrasted with related work in CBR and machine learning (ML).

Master of Science / Department of Computing and Information Sciences / William Hsu / Bayesian networks are graphical models whose nodes represent random variables and whose edges represent conditional dependence between variables. Each node in a Bayesian network is equipped with a conditional probability function that expresses the likelihood that the node will take on different values given the values of its parents. A common task for a Bayesian network is to perform inference by computing the marginal probabilities of each possible value for each node. In this thesis, I introduce three new algorithms for approximate inference of Bayesian networks that use edge deletion techniques. The first reduces a network to its maximal weight spanning tree using the Kullback-Leibler information divergence as edge weights, and then runs Pearl’s algorithm on the resulting tree. Because Pearl’s algorithm can perform inference on a tree in linear time, as opposed to the exponential running time of all general exact inference algorithms, this reduction results in a tremendous speedup in inference. The second algorithm applies triangulation pre-processing rules that are guaranteed to be optimal if the original graph has a treewidth of four or less, and then deletes edges from the network and continues applying rules so that the resulting triangulated graph will have a maximum clique size of no more than five. The junction tree exact inference algorithm can then be run on the reduced triangulated graph. While the junction tree algorithm has an exponential worst-case running time in the size of the maximum clique in the triangulated graph, placing a bound on the clique size effectively places a polynomial time bound on the inference procedure. The third algorithm deletes edges from a triangulation of the original network until the maximum clique size in the triangulated graph is below a desired bound. Again, the junction tree algorithm can then be run on the resulting triangulated graph, and the bound on the maximum clique size will also polynomially bound the inference time. When tested for efficiency and accuracy on common Bayesian networks, these three algorithms perform up to 10,000 times faster than current exact and approximate techniques while achieving error values close to those of sampling techniques.

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions.