Add to ORKGBayesian estimators are defined in terms of the posterior distribution. Typically, this is written as the product of the likelihood function and a prior probability density, both of which are assumed to be known. But in many situations, the prior density is not known, and is difficult to learn from data since one does not have access to uncorrupted samples of the variable being estimated. We show that for a wide variety of observation models, the Bayes least squares (BLS) estimator may be formulated without explicit reference to the prior. Specifically, we derive a direct expression for the estimator, and a related expression for the mean squared estimation error, both in terms of the density of the observed measurements. Each of these prio...
Bayesian prediction is analyzed in the I.I.D case. In a search for robust methods we combine non par...
A common assumption in statistics is that a random sample from a target distribution is available. B...
l Statistical inference concerns unknown parameters that describe certain population characteristics...
This paper presents in a simple and unified framework the Least-Squares approximation of posterior e...
When dealing with Bayesian inference the choice of the prior often remains a debatable question. Emp...
When dealing with Bayesian inference the choice of the prior often remains a debatable question. Emp...
The paper presents in a simple and unified framework the least-Squares approximation of posterior ex...
Introduction In the Bayesian approach to statistical inference the posterior distribution summarize...
We study the Bayesian approach to nonparametric function estimation problems such as nonparametric r...
Introduction Central in Bayesian statistics is Bayes' theorem, which can be written as follows...
To my mother and the loving memory of my father Bayesian filtering refers to the process of sequenti...
This thesis explores how a Bayesian should update their beliefs in the knowledge that any model ava...
We show that Bayes estimators of an unknown density can adapt to unknown smoothness of the density. ...
Prior specification for nonparametric Bayesian inference involves the difficult task of quan-tifying...
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...
A common assumption in statistics is that a random sample from a target distribution is available. B...
l Statistical inference concerns unknown parameters that describe certain population characteristics...
This paper presents in a simple and unified framework the Least-Squares approximation of posterior e...
When dealing with Bayesian inference the choice of the prior often remains a debatable question. Emp...
When dealing with Bayesian inference the choice of the prior often remains a debatable question. Emp...
The paper presents in a simple and unified framework the least-Squares approximation of posterior ex...
Introduction In the Bayesian approach to statistical inference the posterior distribution summarize...
We study the Bayesian approach to nonparametric function estimation problems such as nonparametric r...
Introduction Central in Bayesian statistics is Bayes' theorem, which can be written as follows...
To my mother and the loving memory of my father Bayesian filtering refers to the process of sequenti...
This thesis explores how a Bayesian should update their beliefs in the knowledge that any model ava...
We show that Bayes estimators of an unknown density can adapt to unknown smoothness of the density. ...
Prior specification for nonparametric Bayesian inference involves the difficult task of quan-tifying...
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...
A common assumption in statistics is that a random sample from a target distribution is available. B...
l Statistical inference concerns unknown parameters that describe certain population characteristics...