Posterior consistency of logistic Gaussian process priors in density estimation

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POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, methodology): “One approach to nonparametric density estimation is based on Gaussian process priors; see, for example, Tokdar and Ghosh (2007), Tokdar (2007), van der Vaart and van Zanten (2008), Liang, Carlin, and Gelfand (2009), and Tokdar, Zhu and Ghosh (2010).”
On Bayesian supremum norm contraction rates (2013, background): “Ghosal, Ghosh and van der Vaart [12], Scricciolo [28], Tokdar and Ghosh [34], van der Vaart and van Zanten [37], [3], Rivoirard and Rousseau [26], and random dyadic histograms, for random histogram priors see e.”
State Space Gaussian Processes with Non-Gaussian Likelihood (2018, background): “This model reaches posterior consistency in the limit of bin width going to zero ( N → ∞ ) (Tokdar & Ghosh, 2007).”
Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, background): “Of importance to us is the result in Tokdar and Ghosh (2007) that the support of ω(i, τ) includes all continuous or piecewise continuous functions.”