Sparse Bayesian infinite factor models.

By Anirban Bhattacharya, David B. Dunson. Biometrika, 2011

519 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/822391a297b7baeb3aa5e194e1daaff93899f553

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Citations in the corpus

Hierarchical Shrinkage Priors for Regression Models (2017, methodology): “Hierarchical shrinkage priors have been also used in other areas such as factor models (Bhattacharya and Dunson, 2011).”
Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context (2017, methodology): “As described inmore detail in Bhattacharya & Dunson (2011), this prior allows for uncertainty in the number of factors and sparsity structure.”
Bayesian nonparametric vector autoregressive models (2018, unknown): “We use the multiplicative gamma process shrinkage prior of Bhattacharya and Dunson (2011) forΛ.”
So Many Variables: Joint Modeling in Community Ecology. (2015, methodology): “This trade-off can bemanaged by standard model selection approaches such as information criteria [33], or (in a Bayesian framework) by using a model that automatically shrinks less-informative latent variables to zero [39].”
On the Frequentist Properties of Bayesian Nonparametric Methods (2016, background): “Bhattacharya and Dunson, 2011.”
A note on the multiplicative gamma process (2016, methodology): “…the probabilistic characterization of the multiplicative gamma process and add further insights on the properties of this prior compared to those available in Bhattacharya and Dunson (2011), including simple strategies to check whether speciﬁc hyperparameter choices actually facilitate shrinkage.”
Bayesian group factor analysis with structured sparsity (2016, methodology): “More recently, Bayesian shrinkage methods using sparsity-inducing priors have been introduced for latent factor models (Archambeau and Bach, 2009; Carvalho et al., 2008; Virtanen et al., 2012; Bhattacharya and Dunson, 2011; Klami et al., 2013).”
Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes (2017, methodology): “Finally, we note that this approach for generating a reduced rank matrix A, differs considerably from the approach in Bhattacharya and Dunson (2011).”
Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB (2024, unknown): “Another alternative for controlling complexity is some form of shrinkage towards zero on the factor loadings as proposed in a Bayesian framework (Bhattacharya and Dunson 2011).”
Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales (2016, methodology): “We parameterized the prior (for all three spatial levels) as kkj N 0;/ 1kj s 1k ;/kj Gamma 3=2; 3=2ð Þ; sk ¼ Yk l¼1 dl; dl Gamma 50; 1ð Þ: As described in more detail in Bhattacharya & Dunson (2011), this prior allows for uncertainty in the number of factors and sparsity structure.”
Adaptive Shrinkage in Bayesian Vector Autoregressive Models (2019, methodology): “The first two modifications of the NG prior have recently been applied in Kastner (2016) in a factor stochastic volatility framework while the third modification is related to the multiplicative Gamma process prior proposed in Bhattacharya and Dunson (2011) within a static factor framework.”