Easy and Efficient Bayesian Infinite Factor Analysis

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2020

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Abstract

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 https://github.com/poworoznek/infinitefactor and in release version on the CRAN package repository.

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Poworoznek, Evan (2020). Easy and Efficient Bayesian Infinite Factor Analysis. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/20832.

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