49 citations · 196 across the 17 of their papers we have counts for
17 papers
Generalized reparametrized variational Bayes with skew-symmetric normalization
Aoxiang Chen, Linda S. L. Tan
Bayesian hierarchical models with high-dimensional latent structure require scalable posterior approximations that preserve key dependencies while remaining computationally tractab…
Weighted Fisher divergence for high-dimensional Gaussian variational inference
Aoxiang Chen, David J. Nott, Linda S. L. Tan
Bayesian inference has many advantages for complex models, but standard Monte Carlo methods for summarizing the posterior can be computationally demanding, and it is attractive to…
Variational inference based on a subclass of closed skew normals
Linda S. L. Tan, Aoxiang Chen
Gaussian distributions are widely used in Bayesian variational inference to approximate intractable posterior densities, but the ability to accommodate skewness can improve approxi…
Second order stochastic gradient update for Cholesky factor in Gaussian variational approximation from Stein's Lemma
Linda S. L. Tan
In stochastic variational inference, use of the reparametrization trick for the multivariate Gaussian gives rise to efficient updates for the mean and Cholesky factor of the covari…
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation
Linda S. L. Tan
Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in…
Conditionally structured variational Gaussian approximation with importance weights
Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott
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…
Bayesian variational inference for exponential random graph models
Linda S. L. Tan, Nial Friel
Deriving Bayesian inference for exponential random graph models (ERGMs) is a challenging "doubly intractable" problem as the normalizing constants of the likelihood and posterior d…
Explicit inverse of tridiagonal matrix with applications in autoregressive modeling
Linda S. L. Tan
We present the explicit inverse of a class of symmetric tridiagonal matrices which is almost Toeplitz, except that the first and last diagonal elements are different from the rest.…
Efficient data augmentation techniques for some classes of state space models
Linda S. L. Tan
Data augmentation improves the convergence of iterative algorithms, such as the EM algorithm and Gibbs sampler by introducing carefully designed latent variables. In this article,…
Dynamic degree-corrected blockmodels for social networks: a nonparametric approach
Linda S. L. Tan, Maria De Iorio
A nonparametric approach to the modeling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmo…
Gaussian variational approximation with sparse precision matrices
Linda S. L. Tan, David J. Nott
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 m…
Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
Linda S. L. Tan, Ajay Jasra, Maria De Iorio +1
We investigate the effect of cadmium (a toxic environmental pollutant) on the correlation structure of a number of urinary metabolites using Gaussian graphical models (GGMs). The i…
Topic-adjusted visibility metric for scientific articles
Linda S. L. Tan, Aik Hui Chan, Tian Zheng
Measuring the impact of scientific articles is important for evaluating the research output of individual scientists, academic institutions and journals. While citations are raw da…
Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes
Linda S. L. Tan
Discrete choice models describe the choices made by decision makers among alternatives and play an important role in transportation planning, marketing research and other applicati…
Variational inference for sparse spectrum Gaussian process regression
Linda S. L. Tan, Victor M. H. Ong, David J. Nott +1
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 a…
A stochastic variational framework for fitting and diagnosing generalized linear mixed models
Linda S. L. Tan, David J. Nott
In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets…
Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations
Linda S. L. Tan, David J. Nott
The effects of different parametrizations on the convergence of Bayesian computational algorithms for hierarchical models are well explored. Techniques such as centering, noncenter…