Regression Density Estimation With Variational Methods and Stochastic Approximation

By David J. Nott, Siew Li Tan, Mattias Villani, Robert Kohn. 2012

60 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, methodology): “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).”
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression (2012, methodology): “While our method somewhat resembles performing stochastic gradient descent on the variational objective function in parameter space (Paisley et al., 2012; Nott et al., 2012), the linear regression view gives insights which allow a more computationally eﬃcient approach.”
Approximate Bayesian computation via regression density estimation (2012, methodology): “Methods for fast and parsimonious fitting of (3) can be found in Nott et al. (2012) and Tran et al. (2012).”
Gaussian variational approximation with sparse precision matrices (2016, methodology): “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”
On Using Control Variates with Stochastic Approximation for Variational Bayes and its Connection to Stochastic Linear Regression (2014, background): “…only do so analytically for a very limited set of approximations and posteriors, so several authors (Paisley et al., 2012; Ranganath et al., 2013; Nott et al., 2012; Kingma and Welling, 2013; Hoﬀman et al., 2012; Wingate and Weber, 2013) have recently proposed addressing this expectation using…”
Structured Stochastic Variational Inference (2014, background): “Ji et al. (2010); Nott et al. (2012); Gerrish (2013); Paisley et al. (2012), and Ranganath et al. (2014) proposed sampling without a change of variables as a way of coping with non-conjugacy.”
Variance reduction properties of the reparameterization trick (2018, methodology): “Copyright 2019 by the author(s). econometrics and statistics, complex regression density estimation (Nott et al., 2012), state space models (Tan and Nott, 2018), and high-dimensional time-varying parameter models (Quiroz et al., 2018) are approximated using VI.”
A Stochastic Variational Framework for Fitting and Diagnosing Generalized Linear Mixed Models (2012, methodology): “Tan and Nott (2012) proposed ﬁtting GLMMs with a NCVMP algorithm that iterates between updating variational parameters of the local variables and re-estimating variational parameters of the global variables.”