Gaussian variational approximation with sparse precision matrices

@article{Tan2018GaussianVA,
  title={Gaussian variational approximation with sparse precision matrices},
  author={Linda S. L. Tan and D. Nott},
  journal={Statistics and Computing},
  year={2018},
  volume={28},
  pages={259-275}
}
  • Linda S. L. Tan, D. Nott
  • Published 2018
  • Mathematics, Computer Science
  • Statistics and Computing
  • We consider the problem of learning a Gaussian variational approximation to the posterior distribution for a high-dimensional parameter, where we impose sparsity in the precision matrix to reflect appropriate conditional independence structure in the model. Incorporating sparsity in the precision matrix allows the Gaussian variational distribution to be both flexible and parsimonious, and the sparsity is achieved through parameterization in terms of the Cholesky factor. Efficient stochastic… CONTINUE READING
    Gaussian Variational Approximation With a Factor Covariance Structure
    28
    Gaussian variational approximation for high-dimensional state space models
    9
    High-dimensional copula variational approximation through transformation
    7
    Fast and Accurate Variational Inference for Models with Many Latent Variables

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 52 REFERENCES
    The Variational Gaussian Approximation Revisited
    220
    Gaussian Variational Approximate Inference for Generalized Linear Mixed Models
    64
    Nonparametric variational inference
    102
    Variational Bayesian Inference with Stochastic Search
    295
    Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
    171
    An Adaptive Learning Rate for Stochastic Variational Inference
    74
    Local Expectation Gradients for Black Box Variational Inference
    58
    Auto-Encoding Variational Bayes
    8809
    Inferring Parameters and Structure of Latent Variable Models by Variational Bayes
    581