Bounded Approximations for Marginal Likelihoods

24 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Variational Inference: A Review for Statisticians (2016, background): “This first appeared independently in several articles (Ji, Shen, and West 2010; Nott et al. 2012; Paisley, Blei, and Jordan 2012; Wingate and Weber 2013).”
Structured Stochastic Variational Inference (2014, methodology): “For example, Ji et al. (2010) use mean-field approximations to the posteriors of “collapsed” models where some parameters have been analytically marginalized out, Salimans and Knowles (2013) apply a structured approximation to the posterior of a stochastic volatility model, and Mimno et al. (2012)…”
A Stochastic Variational Framework for Fitting and Diagnosing Generalized Linear Mixed Models (2012, methodology): “Similar algorithms were considered by Ji et al. (2010) and Nott et al. (2012).”
Regression Density Estimation With Variational Methods and Stochastic Approximation (2012, methodology): “Ji et al. (2010) independently proposed a Monte Carlo stochastic approximation for maximizing the lower bound numerically which uses a similar approach.”
Gaussian variational approximation with sparse precision matrices (2016, background): “There has been much recent research concerned with stochastic gradient methods (Robbins and Monro, 1951) able to optimize L(λ) efficiently in this situation (Ji et al., 2010; Nott et al., 2012; Paisley et al., 2012; Kingma and Welling, 2014; Hoffman et al., 2013; Ranganath et al., 2014; Rezende et a”