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Easy and Efficient Bayesian Infinite Factor Analysis

dc.contributor.advisor Dunson, David B Poworoznek, Evan 2020-06-09T17:45:49Z 2020-06-09T17:45:49Z 2020
dc.description Master's thesis
dc.description.abstract <p>Bayesian latent factor models are key tools for modeling linear structure in data and performing dimension reduction for correlated variables. Recent advances in prior specification allow the estimation of semi- and non-parametric infinite factor mod- els. These models provide significant theoretical and practical advantages at the cost of computationally intensive sampling and non-identifiability of some parameters. We provide a package for the R programming environment that includes functions for sampling from the posterior distributions of several recent latent factor mod- els. These computationally efficient samplers are provided for R with C++ source code to facilitate fast sampling of standard models and provide component sam- pling functions for more complex models. We also present an efficient algorithm to remove the non-identifiability that results from the included shrinkage priors. The infinitefactor package is available in developmental version on GitHub at and in release version on the CRAN package repository.</p>
dc.subject Statistics
dc.subject Factor analysis
dc.subject High-dimensional data
dc.subject Shrinkage
dc.title Easy and Efficient Bayesian Infinite Factor Analysis
dc.type Master's thesis
dc.department Statistical Science

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