Latent Variable Models

@inproceedings{Ravikumar1999LatentVM,
  title={Latent Variable Models},
  author={Pradeep Ravikumar and M. Veloso},
  year={1999}
}
A powerful approach to probabilistic modelling involves supplementing a set of observed variables with additional latent, or hidden, variables. By defining a joint distribution over visible and latent variables, the corresponding distribution of the observed variables is then obtained by marginalization. This allows relatively complex distributions to be expressed in terms of more tractable joint distributions over the expanded variable space. One well-known example of a hidden variable model… Expand
Bayesian inference for latent variable models
Simplex Factor Models for Multivariate Unordered Categorical Data
Building Blocks for Variational Bayesian Learning of Latent Variable Models
Automatic discovery of latent variable models
Diversity-Promoting Bayesian Learning of Latent Variable Models
A New Class of Time Dependent Latent Factor Models with Applications
New d-separation identification results for learning continuous latent variable models
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 26 REFERENCES
Latent Variable Models and Factor Analysis
GTM: The Generative Topographic Mapping
Mixtures of Probabilistic Principal Component Analyzers
A Hierarchical Latent Variable Model for Data Visualization
Probabilistic Principal Component Analysis
Estimation and tests of significance in factor analysis
ASYMPTOTIC THEORY FOR PRINCIPAL COMPONENT ANALYSIS
Hierarchical mixtures of experts and the EM algorithm
  • M. I. Jordan, R. Jacobs
  • Computer Science
  • Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan)
  • 1993
Modeling the manifolds of images of handwritten digits
Magnification factors for the GTM algorithm
...
1
2
3
...