#### DMCA

## Sparse Bayesian infinite factor models

Citations: | 52 - 16 self |

### Citations

131 | Hierarchical beta processes and the indian buffet process. - Thibaux, Jordan - 2007 |

128 | Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data - Gui, Li - 2005 |

114 | Parameter expansion for data augmentation.
- Liu, Wu
- 1999
(Show Context)
Citation Context ...re highly correlated. Also, posterior inference tends to be sensitive to certain hyperparameters, with elicitation difficult. To address these issues, Ghosh and Dunson [2009] use parameter expansion [=-=Liu and Wu, 1999-=-, Gelman, 2006] to induce a heavytailed default prior distribution on the loadings elements and propose an efficient Gibbs sampler. Inference on the number of factors in factor analysis models is both... |

108 | High-dimensional sparse factor modeling: Applications in gene expression genomics. - Carvalho, Chang, et al. - 2008 |

105 | al: The use of molecular profiling to predict survival after chemotherapy for diffuse largeB-cell lymphoma. - Rosenwald, Wright, et al. - 2002 |

102 | On Bayes procedures. - Schwartz - 1965 |

101 |
Measuring the Pricing Error of the Arbitrage Pricing Theory,
- Geweke, Zhou
- 1996
(Show Context)
Citation Context ...ix with non-negative diagonal entries. A popular approach to ensure identifiability of the loadings elements is to constrain the loadings matrix to be lower triangular with positive diagonal entries [=-=Geweke and Zhou, 1996-=-]. Factor models have been traditionally applied in behavioral and social sciences, where the latent factors have a natural interpretation as certain unobserved psychological traits. A more recent app... |

80 | Modeling dyadic data with binary latent factors.
- Meeds, Ghahramani, et al.
- 2007
(Show Context)
Citation Context ... Jordan, 2007]. The Indian buffet process defines a probability distribution on infinite binary matrices, with weighted versions finding applications in factor analysis [Knowles and Ghahramani, 2007, =-=Meeds et al., 2007-=-, Rai and Daumé, 2009]. Our approach can be used as a simpler and more computationally efficient alternative to the Indian buffet process to define priors on latent feature matrices. 2 Bayesian factor... |

61 | Bayesian dynamic factor models and variance matrix discounting for portfolio allocation. - Aguilar, West - 2000 |

60 | Infinite sparse factor analysis and infinite independent components analysis
- Knowles, Ghahramani
- 2007
(Show Context)
Citation Context ... Ghahramani, 2006, Thibaux and Jordan, 2007]. The Indian buffet process defines a probability distribution on infinite binary matrices, with weighted versions finding applications in factor analysis [=-=Knowles and Ghahramani, 2007-=-, Meeds et al., 2007, Rai and Daumé, 2009]. Our approach can be used as a simpler and more computationally efficient alternative to the Indian buffet process to define priors on latent feature matrice... |

52 |
A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis-Hastings algorithm
- Arminger, Muthén
- 1998
(Show Context)
Citation Context ...et al., 2008] uses the above sparse characterization as a dimensionality reduction tool in large p, small n applications such as gene expression studies. A Bayesian specification of the factor model [=-=Arminger and Muthén, 1998-=-, Song and Lee, 2001] commonly uses inverse gamma priors on the residual variances and normal and truncated normal priors on the off-diagonal and diagonal elements of the loadings matrix respectively.... |

52 | Coupling and Ergodicity of Adaptive MCMC - Roberts, Rosenthal - 2007 |

31 | Sparse Statistical Modelling in Gene Expression Genomics 155–176, Cambridge
- Lucas, Carvalho, et al.
- 2006
(Show Context)
Citation Context ...ee and Song [2002] instead developed a path sampling approach. A more recent method infers the number of factors by zeroing a subset of the loadings elements using Bayesian variable selection priors [=-=Lucas et al., 2006-=-, Carvalho et al., 2008, Schnatter and Lopes, 2009]. Ando [2009] proposed an approach for calculating the exact marginal likelihood in Bayesian factor analysis with heavy-tailed priors. This method ca... |

30 |
Stochastic Versions of the EM Algorithm: an Experimental Study
- Celeux, Chauveau, et al.
- 1995
(Show Context)
Citation Context ...ting an approximate maximum a posteriori estimate of the covariance matrix. The proposed approach is useful to arrive at a quick working estimate of the covariance matrix. Our proposed Stochastic EM [=-=Celeux et al., 1996-=-] approach replaces draws from the conditional posterior distributions of Λ˜ k (t), Σ and φ in steps 1, 2 and 4 above by the respective conditional posterior modes. 4 Simulation Example 4.1 Factor sel... |

28 | Default prior distributions and efficient posterior computation in Bayesian factor analysis. - Ghosh, Dunson - 2009 |

17 | Microarray gene expression data with linked survival phenotypes: Diffuse large-B-cell lymphoma revisited - Segal - 2005 |

14 | Bayesian selection on the number of factors in a factor analysis model - Lee, Song - 2002 |

7 | Factor analysis and outliers: A Bayesian approach. Wirtschaftswissenschaftliches Zentrum der Universitt
- Polasek
- 1997
(Show Context)
Citation Context ...s and propose an efficient Gibbs sampler. Inference on the number of factors in factor analysis models is both conceptually and computationally challenging. Some of the early works in this direction [=-=Polasek, 1997-=-] involve computation of the marginal likelihoods under models with different numbers of factors. Lopes and West [2004] proposed a reversible jump Markov chain Monte Carlo algorithm to allow for uncer... |

7 |
Parsimonious bayesian factor analysis
- Fruhwirth-Schnatter, Lopes
- 2009
(Show Context)
Citation Context ... sampling approach. A more recent method infers the number of factors by zeroing a subset of the loadings elements using Bayesian variable selection priors [Lucas et al., 2006, Carvalho et al., 2008, =-=Schnatter and Lopes, 2009-=-]. Ando [2009] proposed an approach for calculating the exact marginal likelihood in Bayesian factor analysis with heavy-tailed priors. This method can be used for rapid estimation of the number of fa... |

3 | Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood - Ando - 2009 |

1 | Bayesian factor regression models in the large p, small n paradigm - Soc - 1996 |