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.
Results
Showing 1 to 20 of 29 for “"Latent Variable Model"”.
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Latent variable model estimation via collaborative filtering
… collaborative filtering under a nonparametric latent variable model, which arises from the natural property of "exchangeability", i.e. invariance under relabeling of the dataset. The analysis suggests that similarity based collaborative filtering can be viewed as kernel regression for latent …
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Theoretical study of two prediction-centric problems : graphical model learning and recommendations
… thesis. PART I. Learning a tree-structured Ising model: We study the problem of learning a tree Ising model from samples such that subsequent predictions based on partial observations are accurate. Virtually all previous work on graphical model learning has focused on recovering the true …
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Approximate Inference in Variational Autoencoders
A deep latent variable model is a powerful tool for modelling complex distributions. However, in order to train this model, we must perform approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent …
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Variational Inference and Probabilistic Models for Parametric Partial Differential Equations
… though variational inference and probabilistic models. The work is composed of three contributions. The first contribution lies in creating active learning surrogates for Bayesian inverse problems called Active learning projected surrogates – SVGD. Here we leverage Stein variational gradient …
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Advances in Hierarchical Probabilistic Multimodal Data Fusion
… formulates reasoning as posterior inference over latent variables. Within the Bayesian setting we present a novel method for data integration that we call lightweight data fusion (LDF). LDF addresses the case where the forward model for a subset of the data sources is unknown or poorly …
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Lensing Machines : representing perspective in machine learning
Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an …
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Algorithms for structural learning with decompositions
… which involve predicting multiple output variables with expressive and complex interdependencies and constraints. Learning over expressive structures (called structural learning) is usually time-consuming as exploring the structured space can be an intractable problem. The goal of this …
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Towards a Comprehensive Model of Musical Ability
… ability, and uses the resulting comprehensive latent measure of musical ability to evaluate previously theorized links between musical ability and individual differences in musical experience, working memory, intelligence, personality factors, and socio-economic status. Results from latent …
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Modeling and Analysis of Repeated Ordinal Data Using Copula Based Likelihoods and Estimating Equation Methods
… discusses the multivariate ordered probit model which is a likelihood method based on latent variables. We show that this latent variable model belong to a very general class of <em>Copula</em> models. We use the copula representation for the multivariate ordered probit model to obtain …
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The Structure of Working Memory: A Review and New View of Psychometric Models
… a central component in most general theories and models of cognition. However, over the years, different researchers have proposed different definitions of WM. This is problematic because researchers who adopt different definitions of WM also tend to administer different kinds of tasks to measure …
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Testing the Measurement Invariance of Large-Scale Assessment of Foreign Language Proficiency Using Multiple Group Covariance Structure Analyses and Item Response Modeling
… the different first language (L1) groups in a latent variable model. This dissertation has the following characteristics: Measurement invariance has been addressed through the methodological sophistication of both simultaneous multiple group covariance structure analyses and item response …
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Sequential data inference via matrix estimation : causal inference, cricket and retail
… of decades. A key component of our work is the latent variable model (LVM) which views the sequential data as a matrix where the rows correspond to multiple sequences while the columns represent the sequential aspect. The goal is to utilize information in the data within the sequence and across …
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Statistical Methods for the Analysis of Contextual Gene Expression Data
… not established. In this thesis, we propose two modelling approaches for the analysis of gene expression variation in specific biological contexts. The first contribution of this thesis is a statistical method for analysing single cell expression data in a spatial context. Our method identifies …
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GTM: the generative topographic mapping
… Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also …
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Optimal anticipatory control as a theory of motor preparation
… We address these questions with a circuit model of movement preparation and control. Specifically, we propose that preparation can be achieved by optimal feedback control (OFC) of the cortical state via a thalamo-cortical loop. Under OFC, the state of the cortex is selectively controlled …
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Robust and interpretable high-dimensional machine learning for predictive cancer medicine
… variational autoencoder (VAE), a probabilistic latent variable model that leverages neural networks to learn latent representations. Specifically, I propose a VAE variant that generates latent representations which explicitly capture genetic dependencies in cancers. I incorporate this into a …
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A stress process model for anxiety symptom severity: comparing racial and ethnic minority adults
… American Survey. The Pearlin Stress Process Model (SPM) provided a theoretical framework for the contributions of stressors and resources to anxiety symptom severity. Method. Pearlin's SPM proposes a latent variable model using Structural Equation Modeling (SEM) to test the relations between …
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Probabilistic modelling of cellular development from single-cell gene expression
… technical limitations. I investigate how to model transcriptional changes during cellular development. The general forms of expression changes with respect to development leads to nonparametric regression models, in the forms of Gaussian Processes. I used Gaussian process models to …
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Statistical methods for the integrative analysis of single-cell multi-omics data
… of multi-omics data sets. MOFA is a Bayesian latent variable model that can be viewed as a statistically rigorous generalization of Principal Component Analysis to multi-omics data. The method provides a principled approach to retrieve, in an unsupervised manner, the underlying sources of …
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Incorporating attitudes in airline itinerary choice : modeling the impact of elapsed time
… Attitudes toward these constructs are latent and cannot be directly observed. In aggregate, we expect an n-shaped utility function for the time additional to the minimum connecting time, with increasing utility close to the minimum connecting time, followed by a time window of …
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