Add to ORKGThis paper presents in a simple and unified framework the Least-Squares approximation of posterior expectations. Particular structures of the sampling process and of the prior distribution are used to organize and to generalize previous results. The two basic structures are obtained by considering unbiased estimators and exchangeable processes. These ideas are applied to the estimation of the mean. Sufficient reduction of the data is analysed when only the Least-Squares approximation is involve
This paper develops a method of obtaining approximate marginal posteriors for all parameters of inte...
In this paper a Bayesian least squares approximation is proposed for descriptive inference in a fini...
We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately from a poste...
The paper presents in a simple and unified framework the Least-Squares approximation of posterior ex...
The paper presents in a simple and unified framework the least-Squares approximation of posterior ex...
Least Squares approximations of posterior axpectations are shown to provide interesting alternatives...
Bayesian prediction is analyzed in the I.I.D case. In a search for robust methods we combine non par...
This paper develops a methodology for approximating the posterior first two moments of the posterior...
in this paper a Bayesian least squares approximation is proposed for the descriptive inference in a ...
Bayesian estimators are defined in terms of the posterior distribution. Typically, this is written a...
textabstractWe propose a general algorithm for approximating nonstandard Bayesian posterior distribu...
Bayesian statistics provides a principled framework for performing statistical inference for an unkn...
Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well s...
<div><p>Bayes’ linear analysis and approximate Bayesian computation (ABC) are techniques commonly us...
Bayes linear analysis and approximate Bayesian computation (ABC) are techniques commonly used in the...
This paper develops a method of obtaining approximate marginal posteriors for all parameters of inte...
In this paper a Bayesian least squares approximation is proposed for descriptive inference in a fini...
We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately from a poste...
The paper presents in a simple and unified framework the Least-Squares approximation of posterior ex...
The paper presents in a simple and unified framework the least-Squares approximation of posterior ex...
Least Squares approximations of posterior axpectations are shown to provide interesting alternatives...
Bayesian prediction is analyzed in the I.I.D case. In a search for robust methods we combine non par...
This paper develops a methodology for approximating the posterior first two moments of the posterior...
in this paper a Bayesian least squares approximation is proposed for the descriptive inference in a ...
Bayesian estimators are defined in terms of the posterior distribution. Typically, this is written a...
textabstractWe propose a general algorithm for approximating nonstandard Bayesian posterior distribu...
Bayesian statistics provides a principled framework for performing statistical inference for an unkn...
Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well s...
<div><p>Bayes’ linear analysis and approximate Bayesian computation (ABC) are techniques commonly us...
Bayes linear analysis and approximate Bayesian computation (ABC) are techniques commonly used in the...
This paper develops a method of obtaining approximate marginal posteriors for all parameters of inte...
In this paper a Bayesian least squares approximation is proposed for descriptive inference in a fini...
We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately from a poste...