Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 56 for “"conditional independence"”.
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The conditional independence assumption in dental developmental age estimation
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01
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Formalizing Causal Models Through the Semantics of Conditional Independence
… this work is a new function-based definition of conditional independence, which captures how changes propagate through a causal graph. We prove that this semantic notion is equivalent to the standard graphical criterion of d-separation, thereby establishing a rigorous bridge between structural …
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Algebro-geometric algorithms for program synthesis, tensor networks, and conditional independence models
Deze dissertatie onderzoekt Algebro-Geometrische Algoritmen in Programmasynthese, Tensornetwerken, en Conditionele Onafhankelijkheidsmodellen. In Programmasynthese draagt het onderzoek bij aan template-gebaseerde synthese voor polynomiale imperatieve programma's, bewijst beslisbaarheid, ontwikkelt …
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The Graphical Representation of Structured Multivariate Data
… fitted models. This leads on to a description of conditional independence graphs, and consideration of the suitability of conditional independence graphs as a technique for the representation of fitted models. Conditional independence graphs are then developed further in accordance with the …
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Common Information and Decentralized Inference with Dependent Observations
… inference is generally intractable with conditional dependent observations. A promising approach for this problem is to utilize a hierarchical conditional independence model. Utilizing the hierarchical conditional independence model, we identify a more general condition under which the …
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Cumulative Distribution Networks: Inference, Estimation and Applications of Graphical Models for Cumulative Distribution Functions
… of functions in the model. We will show that the conditional independence properties in a CDN are distinct from the conditional independence properties of directed, undirected and factor graph models, but include the conditional independence properties of bidirected graphical models. As a result, …
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On the finite sample complexity of causal discovery and the value of domain expertise
… causal discovery under the assumption of a conditional independence (CI) oracle: an oracle that can states whether two random variables are conditionally independent given another set of random variables. Practical implementations of this algorithm incorporate statistical tests for …
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Bayesian Adjustment for Multiplicity
… in linear regresson models, and tests for conditional independence in jointly normal vectors. Multiplicity adjustment in these three areas will be seen to have many common structural features. Though the modeling approach throughout is Bayesian, frequentist reasoning regarding error rates …
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Semiparametric Methods for Two Problems in Causal Inference using Machine Learning
… attention to doubly-robust methods and conditional independence testing. In the second chapter, we explore the doubly-robust estimation of the average partial effect — a generalisation of the linear coefficient in a (partially) linear model and a local measure of causal effect. This …
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Versatile Inference Algorithms using the Bayes Tree for Robot Navigation
… of these methods center on exploiting the conditional independence structure of the problem’s model, they operate directly on graphs instead of their underlying tree decompositions. A Bayes tree, which is the tree decomposition associated with a factor graph, blatantly exposes this sought …
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Hierarchical modelling of multivariate survival data
Hierarchical models based on conditional independence are investigated as a means of modelling multivariate survival times. The model structure follows Clayton (1978), Hougaard (1986b), and Oakes (1986, 1989). Both approximate Bayesian and maximum likelihood estimation in these models is …
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The Impact of Local Item Dependence on Computer Adaptive Testing given Between and Within Testlet Adaptivity
… performance of CAT with testlets was studied. Conditional independence is an essential assumption of Item Response Models. However, when testlets are used, item responses to the items in the same testlet may not be independent since these items are associated with a common stimulus. In this …
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Voice Authenticationa Study Of Polynomial Representation Of Speech Signals
… HMMs impose a requirement that each frame has conditional independence from the next. However, at a fixed frame rate, typically 10 ms., the adjacent feature vectors might span the same phonetic segment and often exhibit smooth dynamics and are highly correlated. The relationship between …
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Direct Simulation Methods for Multiple Changepoint Problems.
… This class of models satisfy an important conditional independence property. This algorithm enables simulation from the true joint posterior distribution of the number and position of the changepoints for a class of changepoint models. The computational cost of this exact algorithm is …
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Algebraic Geometry of Bayesian Networks
… we study the algebraic varieties defined by the conditional independence statements of Bayesian networks. A complete algebraic classification, in terms of primary decomposition of polynomial ideals, is given for Bayesian networks on at most five random variables. Hidden variables are related to …
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On the low-dimensional structure of Bayesian inference
… the posterior distribution might satisfy conditional independence assumptions that reflect local probabilistic interactions; and in some cases we might be uninterested in the posterior distribution per se, but rather in specific prediction goals. In this thesis we: (1) provide a rigorous …
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Maximal Correlation Feature Selection and Suppression With Applications
… feature suppression via enforcing marginal and conditional independence criteria with respect to a sensitive attribute, and illustrate the effectiveness of our methods to problems of fairness, privacy, and transfer learning. Finally, we explore the use of HGR in extracting features for outlier …
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A new constraint-based algorithm to learn Bayesian network structure from data: Control of Spurious Pairwise Information (CSPI)
… from data requires an exponential number of conditional independence tests; several algorithms have been proposed in order to reduce the runtime of this procedure. We present a new constraint-based algorithm for learning Bayesian network structure from data, based on Control of Spurious …
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Trees and beyond : exploiting and improving tree-structured graphical models
… provide a powerful framework for encoding such conditional independence structure of a large collection of random variables. A special class of graphical models with significant theoretical and practical importance is the class of tree-structured graphical models. Tree models have several …
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