Nonparametric binary regression using a Gaussian process prior

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Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, background): “Some notable semiparametric approaches which focus on relaxing the normality assumption include Basu and Mukhopadhyay (2000) and Newton et al. (1996), while others have targeted the linearity assumption (e.g., Mukhopadyay and Gelfand, 1997; Walker and Mallick, 1997; Choudhuri et al., 2007).”
Bayesian inference with rescaled Gaussian process priors (2007, methodology): “sian process W with covariance function EWsWt = aexp(−b|t− s|2) for some a,b>0. The existing mathematical literature concerned with priors of this type focusses on computational issues (e.g. [16], [1], [10]) or posterior consistency ([5]). In the present paper we study posterior convergence rates”
Rates of contraction of posterior distributions based on Gaussian process priors (2008, background): “Several other constructions have also been considered in the literature, for density estimation as well as other statistical problems; see Section 3 and [4, 5, 9, 12, 15, 19, 20, 21, 22, 26, 29, 34].”