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 188 for “"latent variable"”.
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Latent variable augmentation for approximate Bayesian inference
… for sampling methods, and strongly correlated variables quickly become a bottleneck for many inference algorithms. Instead of developing yet another new state-of-the-art sampler or optimizer, we focus on reinterpreting models such that standard inference algorithms like blocked Gibbs sampling, …
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Latent variable models for hippocampal sequence analysis
Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Indeed, the activity of ensembles of neurons within the hippocampus is thought to enable memory formation, storage, recall, and even decision making. Replay is observed during …
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Latent variable interaction in structural equation models
… investigated moderated regression analysis using latent variables. Interactive psychological theories, the stress-buffering hypothesis, and interaction in regression and structural equation models, were reviewed. Moderated regression was recast using latent variables to accommodate measurement …
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Advances in Latent Variable and Causal Models
… and has application to the problem of causal variable definition. This work has broad implications to the causal modelling process in general, as there is often a mismatch between the level at which measurements are made and the level at which the underlying ‘true’ causal structure exists, yet …
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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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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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Complex latent variable modeling for multivariate hierarchical data
… independent chapters, the stepwise multilevel latent class analysis with covariates methodological framework. In recent decades, the latent class analysis methodological scholarship has developed sophisticated methodologies, including innovative estimation approaches and open-source software …
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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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A Test of a Model of Sexual Victimization: A Latent Variable Path Analysis
… sexual victimization. Hypothesized mediating variables were negative cognitive schemas, dissociation, risky behaviors, and coping strategies. Structural equation modeling was used to examine two competing models of sexual victimization. A sample of 276 college students taking introductory …
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Programmatically Extending Emergency Notifications into Regional Online Social Networks with Latent Variable Inference Techniques
<p>Amidst the growing use of social media platforms as actively redundant communication channels, emergency alerting via this pathway is in a position to gain from a more automated and systematic usage approach. The corresponding growth of geographic information systems/services (GIS) in local …
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The Effects of Parcels and Latent Variable Scores on the Detection of Interactions in Structural Equation Modeling
… sciences involve the regression of an outcome variable on component terms and their product to evaluate interaction effects. There are numerous statistical difficulties with this multiple regression approach. The most serious is measurement error, requiring the use of structural equation …
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Probabilistic latent variable modeling for predicting future well-being and assessing behavioral influences on mood, stress and health
In recent years, there has been a shift in the psychological research literature from an emphasis on dysfunction to a focus on well-being and positive mental health. As a result, enhancing well-being in individuals has become a viable approach to improving health, in addition to treating disorders …
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Coping Efforts and Efficacy, Acculturation, and Post-Traumatic Symptomatology in Adolescents following Wildfire: A Latent Variable Path Analysis
… measures 3- and 10- months after the fires, in a latent variable path analysis to assess the fit of a model including exposure/loss, coping efficacy, coping strategy, and PTSD, depression, and anxiety scores. Moreover, acculturation level and SES were included along with gender and ethnicity in …
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