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 19 of 19 for “"hidden variables"”.
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Modelling the Baltic Sea food web with a Dynamic Bayesian Network with hidden variables
… These changes may be driven by unobserved variables, i.e. ecosystem components that we do not have data on. This thesis fits a Dynamic Bayesian Network (DBN) model to one such ecosystem, the Baltic Sea. Three versions of a DBN of the Gotland Basin food web are fitted to data, to evaluate …
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Algebraic Geometry of Bayesian Networks
… for Bayesian networks on at most five random variables. Hidden variables are related to the geometry of higher secant varieties. Moreover, a complete algebraic classification, in terms of generating sets of polynomial ideals, is given for Bayesian networks on at most three random variables and …
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Unsupervised modeling of latent topics and lexical units in speech audio
… identity of each discovered pattern as hidden variables. By applying an Expectation-Maximization (EM) algorithm, our method estimates the latent probability distributions over the pseudo-words and topics associated with the discovered patterns. Using this information, we produce …
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Two new approaches for learning Hidden Markov Models
Hidden Markov Models (HMMs) are ubiquitously used in applications such as speech recognition and gene prediction that involve inferring latent variables given observations. For the past few decades, the predominant technique used to infer these hidden variables has been the Baum-Welch algorithm. …
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Content modeling for social media text
… attribute tendencies are represented as hidden variables. The model explains how the observed text arises from the latent variables, thereby connecting text fragments with corresponding properties and attributes.
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Fundamental aspects of imaging matrix assisted laser desorption ionisation mass spectrometry.
… have been examined in relation to extracting hidden variables from multidimensional image data sets.
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Object recognition with latent Conditional Random Fields
… extension of the CRF framework that incorporates hidden variables and combines class conditional CRFs into a unified framework for part-based object recognition. The random field captures spatial coherence between region labels. The parameters of the CRF are estimated in a maximum likelihood …
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Limits to extreme event forecasting in chaotic systems
… sources: uncertainty in the initial conditions, hidden variables, and suboptimal modeling assumptions. The latter allows us to assess whether prediction models are operating near their maximum theoretical performance or if further improvements are possible. The bounds are applied to the …
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Quantum Correlations for Fundamental Tests and Quantum Communication
… experiments has convinced physicists that local hidden variables cannot describe the correlations arising from measurements on entangled particles. On the application side, quantum correlations have been used in quantum key distribution, which aims to provably secure messages during transmission, …
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In search of functional specificity in the brain : generative models for group fMRI data
… of specificity corresponds to inference on the hidden variables of the model based on the observed fMRI data. We also develop a nonparametric hierarchical Bayesian model for group fMRI data that integrates the mixture model prior over activations with a model for fMRI signals. We apply the …
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An Indeterminacy-based Local Approach and Ontology for Quantum Theory
… without modifying the quantum formalism, adding hidden variables, or adopting relationalism. Second, I propose Generative Quantum Theory (GQT) as a new ontology for quantum theories and show how it can be implemented via GRW, the MWI and single-world relationalist views, Bohmian Mechanics, hybrid …
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Computational Models of Function and Evolution of cis-Regulatory Sequences
… Through a statistical approach that marginalizes hidden variables, the method is able to deal with the uncertainty of sequence alignment and prediction of individual TFBSs, two primary technical hurdles of existing methods. In a related work, I collaborated with a graduate colleague to study the …
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Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
… approximate posterior families for the hidden variables in these models are studied. We also discuss several methods for approximate uncertainty propagation in recurrent and deep architectures based on Gaussian projection, linearisation, and simple Monte Carlo. The benefit of the unified …
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Anomaly Detection via Latent Variables Learned by Variational Autoencoders
… art modeling techniques that incorporate latent variables, hidden variables that are not directly observed but instead inferred from observed variables. Approaches to anomaly detection via variational autoencoders either adopt reconstruction error as a sole anomaly detection metric, ignoring the …
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Computational Modeling for Differential Analysis of RNA-seq and Methylation data
… combines the differential states of isoforms as hidden variables for differential analysis. The differential states of isoforms are estimated jointly with other model parameters through a sampling process, providing an improved performance in detecting isoforms of less differentially expressed. …
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Maximum likelihood estimation in dynamical systems
Short description of contents: <br> <br>Dynamical systems are frequently used to model the time evolution of a system. <br>Typically, they depend on unknown parameters and their trajectories <br>are only partially observed. <br>Here maximum likelihood methods are used for the estimation of the …
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Introducing a Kantian interpretation of quantum physics : in accordance with Kant's philosophy of science in the Critique of the power of judgment, reinterpreted and reworked with special attention to the supersensible realm
… Bell inequality that is not dependent on local hidden variables and showed that the violation thereof negates even what might be called "stochastic" determinism - at least in the framework of the Lorentzian space-time manifold (Redhead 1987:83, 103). With the acceptance of non-determinism as …
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Theoretical guarantees and complexity reduction in information planning
… that would reduce the uncertainty over latent variables of interest under a set of constraints. A commonly used reward for quantifying the expected reduction in uncertainty is mutual information (MI). One application of information planning can be found in object tracking where a network of …
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Polarization Sensitive Imaging Techniques Using Quantum Entangled Qubits
… of physical reality and the requirement of hidden variables. The phenomenon involved quantum entanglement and it opened opportunities for research in numerous fields of study. In optical communication entangled states were applied to quantum information theory, quantum teleportation and …