Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Linda S. L. Tan"'
Autor:
Nial Friel, Linda S. L. Tan
Deriving Bayesian inference for exponential random graph models (ERGMs) is a challenging "doubly intractable" problem as the normalizing constants of the likelihood and posterior density are both intractable. Markov chain Monte Carlo (MCMC) methods w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68a5d52e7c53bdaae2f2d494a2414782
Autor:
Maria De Iorio, Linda S. L. Tan
Publikováno v:
Statistical Modelling. 19:386-411
A nonparametric approach to the modelling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmodel using a probit regression formulation, and popularity parameters
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::116bcf913e1876e000d0ef8cf865237c
https://doi.org/10.1177/1471082x18770760
https://doi.org/10.1177/1471082x18770760
Autor:
Linda S. L. Tan
Publikováno v:
IMA Journal of Applied Mathematics.
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. This class of tridiagonal matrices is of special interest in complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d69f1d20169c937c184effc29b09c851
https://doi.org/10.1093/imamat/hxz010
https://doi.org/10.1093/imamat/hxz010
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, we define a class of variational approx
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea3505284e0de1c6d3785a6c833d03ec
Autor:
Linda S. L. Tan
We propose using model reparametrization to improve variational Bayes inference for hierarchical models whose variables can be classified as global (shared across observations) or local (observation-specific). Posterior dependence between local and g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e302128a9daf7111ed4671507a02d0e6
Autor:
David J. Nott, Linda S. L. Tan
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c45cc4843958e0ca9c0dcea43398663
http://arxiv.org/abs/1605.05622
http://arxiv.org/abs/1605.05622
Publikováno v:
Ann. Appl. Stat. 10, no. 1 (2016), 1-31
Measuring the impact of scientific articles is important for evaluating the research output of individual scientists, academic institutions and journals. While citations are raw data for constructing impact measures, there exist biases and potential
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc2a5265a26dcd2dea15be22624f5b8d
https://doi.org/10.1214/15-aoas887
https://doi.org/10.1214/15-aoas887
Publikováno v:
Electron. J. Statist. 10, no. 1 (2016), 527-549
This paper considers functional models for longitudinal data with subject and group specific trends modelled using Gaussian processes. Fitting Gaussian process regression models is a computationally challenging task, and various sparse approximations
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e2bf9e7d978d6c4bad61441bc8ea584
http://projecteuclid.org/euclid.ejs/1457123505
http://projecteuclid.org/euclid.ejs/1457123505
Publikováno v:
The Annals of Applied Statistics
Ann. Appl. Stat. 11, no. 4 (2017), 2222-2251
Ann. Appl. Stat. 11, no. 4 (2017), 2222-2251
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 inferred metabolic associations can provide important information on th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faf7114688e1622666c3264adcdd72f6
Autor:
Linda S. L. Tan, David J. Nott
Publikováno v:
Bayesian Anal. 9, no. 4 (2014), 963-1004
In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets of data are used to approximate the true gradient over the whole data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91d8ddf98a1f94562a6343bf0a2801d6
https://doi.org/10.1214/14-ba885
https://doi.org/10.1214/14-ba885