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 40 for “"latent variable models"”.
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Latent variable models for hippocampal sequence analysis
… PBEs. More specifically, I use hidden Markov models (HMMs) to study PBEs observed in rats during exploration of both linear tracks and open fields, and I demonstrate that estimated models are consistent with a spatial map of the environment. Moreover, I demonstrate how the model can be used to …
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Continuous-time Latent-variable Models for Time Series
… the most powerful approaches for time series is latent-variable models, thanks to their ability to handle multi-dimensional data with complex interactions. Typically, these models represent a timeline as a sequence of discrete states and therefore assume that observations occur at regular …
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Embedding and latent variable models using maximal correlation
Finding low dimensional latent variable models is a useful technique in inferring unobserved affinity between unobserved co-occurrences. We explore using maximal correlation and the alternating conditional expectation algorithm to construct embeddings one dimensional at a time to maximally preserve …
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Probabilistic latent variable models for knowledge discovery and optimization
I conduct a systematic study of probabilistic latent variable models (PLVMs) with applications to knowledge discovery and optimization. Probabilistic modeling is a principled means to gain insight of data. By assuming that the observed data are generated from a distribution, we can estimate its …
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Latent variable models for understanding user behavior in software applications
… user's workflow. In this thesis, I propose novel latent variable models to understand, predict and eventually automate the user interaction with a software application. I start by analyzing users' clicks using time series models; I introduce models and inference algorithms for time series …
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Latent Variable Models for Longitudinal Outcomes from a Parenting Intervention Study
… modelling (SEM) for longitudinal profiles and latent growth mediation modelling. Improved behaviour was observed in terms of reported child behaviour problems and reported harsh parenting with differences between the intervention and control groups directly after the completion of the 3-month …
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Blind regression : nonparametric regression for latent variable models via collaborative filtering
… features x = (x1(u), x2(i)) are not observed (latent), making it challenging to apply standard regression methods. We suggest a two-step procedure to overcome this challenge: 1) estimate distance for latent variables, and then 2) apply nonparametric regression. Applying this framework to matrix …
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On Latent Variable Models for Bayesian Inference with Stable Distributions and Processes
… Bayesian inference schemes for two different latent variable models, with the aim of providing guarantees of accuracy when the $\alpha$-stable model is used. In the first part of the thesis, a marginal representation of the $\alpha$-stable density is used to develop a novel, asymptotically …
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Geographic and Social Space in Latent Factor Models - Four Essays
… of human interaction. When building statistical models, their consideration is vital: They all cause dependency between individual observations, violating assumptions of independence and exchangeability. While this can be problematic and inhibit the unbiased inference of parameters, it can also …
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Modelling Multivariate Nonlinear Vaccine Induced Immune Responses
Interpretable statistical models for multivariate vaccine induced immune response data are important as they provide a rigorous means of deciding which vaccine candidates should be advanced in the clinical trials process. We consider applications of several different statistical models to a vaccine …
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Estimation, Prediction and Counterfactual Inference with Dependent Observations
… encompass both fully observable as well as latent variable models. For fully observable models, we use the celebrated Ising model to describe the dependencies. Assuming we have access to a single sample from some Ising model, which captures a variety of real-world scenarios, we design and …
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Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… applications of our nonparametric Bayesian latent variable models to real problems in science and engineering, including count modeling, text analysis, image processing, compressive sensing, and computer vision.</p>
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Generalised Bayesian matrix factorisation models
Factor analysis and related models for probabilistic matrix factorisation are of central importance to the unsupervised analysis of data, with a colourful history more than a century long. Probabilistic models for matrix factorisation allow us to explore the underlying structure in data, and have …
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Methods and Theory for Joint Estimation of Incidental and Structural Parameters in Latent Class Models
… become the standard for parameter estimation in latent variable models. However, there are instances when alternative estimators that jointly estimate incidental parameters and structural parameters might be easier to implement. A drawback to joint estimation, and joint maximum likelihood …
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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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Deep Learning Tools for Next-Generation Connectomics
… and functional data with connectome-constrained latent variable models (CC-LVMs) of the unobserved voltage dynamics for the whole-brain nervous system. We hope these advanced applications of deep learning techniques will help address the performance and accuracy trade-offs of next-generation …
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Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction
… guide treatment decisions. While deep learning models such as LSTMs have demonstrated strong predictive performance on multivariate clinical time series, they often lack interpretability. To address this gap, this thesis proposes a framework that combines the predictive strength of neural …
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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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