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  1. No Access

    Article

    Conditionally structured variational Gaussian approximation with importance weights

    We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into “global” parameters and “local” latent variables...

    Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott in Statistics and Computing (2020)

  2. No Access

    Article

    Gaussian variational approximation with sparse precision matrices

    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 ...

    Linda S. L. Tan, David J. Nott in Statistics and Computing (2018)

  3. No Access

    Article

    Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes

    Discrete choice models describe the choices made by decision makers among alternatives and play an important role in transportation planning, marketing research and other applications. The mixed multinomial lo...

    Linda S. L. Tan in Statistics and Computing (2017)

  4. No Access

    Article

    Variational inference for sparse spectrum Gaussian process regression

    We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation. Our approach enables uncertainty i...

    Linda S. L. Tan, Victor M. H. Ong, David J. Nott, Ajay Jasra in Statistics and Computing (2016)