On Bayes procedures

By L. Schwartz. 1965

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Citations in the corpus

Finite Sample Bernstein -- von Mises Theorem for Semiparametric Problems (2013, background): “mena arising in the Bayesian analysis when the parameter dimension increases. Already consistency of the posterior distribution in nonparametric and semiparametric models is a nontrivial problem; cf. Schwartz (1965) and Barron et al. (1996). Asymptotic normality of the posterior measure for these cl”
Posterior consistency for semi-parametric regression problems (2003, methodology): “If we write (2.5) as —(WjY1, . . . , Yn) ¼ I1n(Y1, . . . , Yn) I2n(Y1, . . . , Yn) , (2:6) the proof involves showing, as is done in Schwartz (1965), that condition (i) implies that there exists a d .”
On the Frequentist Properties of Bayesian Nonparametric Methods (2016, methodology): “This was initiated by the work of (Schwartz, 1965) in the case of density estimation and extended by (Barron, 1988) for generic models.”
Conditioning, Likelihood, and Coherence: A Review of Some Foundational Concepts (2000, background): “Consistency is sometimes attainable and sometimes not (see, e.g., Barron 1988; Barron, Schervish, and Wasserman 1999; Diaconis and Freedman 1986, 1990, 1993, 1997; Doob 1949; Freedman 1963, 1965; Freedman and Diaconis 1983; Ghosal, Ghosh, and Ramamoorthi 1999; Schwartz 1960, 1965) for example.”
Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, methodology): “ addition to being a desirable property on its own, the ramications of large support are signicant, as it is a key condition which is used in the study of posterior consistency, under the theory of Schwartz (1965). The KL divergence is used as a measure of distance, so that a true density f 0(w) is ”
Convergence rates of posterior distributions for non-i.i.d. observations (2007, background): “If the observations are independent and identically distributed (i.i.d.), the issue of consistency was addressed by Schwartz (1965) , Barron, Schervish and Wasserman (1999), Ghosal, Ghosh and Ramamoorthi (1999a, b), Wasserman (1998), Hjort (2002) and Ghosh and Ramamoorthi (2003).”
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, methodology): “A general weak posterior consistency theorem for density estimation was established by Schwartz (1965).”
Critical dimension in the semiparametric Bernstein—von Mises theorem (2014, unknown): “[25, 1]).”
Consistency of Bernstein polynomial posteriors (2002, background): “Theorem 2.”