In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adaptive estimation via a prior distribution that does not depend on the regularity of the function to be estimated nor on the sample size is valuable. We elucidate relationships among the main approaches followed to design priors for minimax-optimal rate-adaptive estimation meanwhile shedding light on the underlying ideas
We consider the problem of estimating the mean of an infinite-dimensional normal distribution from t...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adapti...
In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adapti...
Summary: We consider estimating a probability density p based on a random sample from this density b...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider estimating a probability density p based on a random sample from this density by a Bayes...
We consider estimating a probability density p based on a random sample from this density by a Bayes...
We consider estimating a probability density p based on a random sample from this density by a Bayes...
International audienceWe derive rates of contraction of posterior distributions on non-parametric mo...
International audienceWe derive rates of contraction of posterior distributions on non-parametric mo...
We derive rates of contraction of posterior distributions on nonparametric models resulting from sie...
working paper : http://arxiv.org/abs/1204.2392v2We derive rates of contraction of posterior distribu...
We consider the problem of estimating the mean of an infinite-dimensional normal distribution from t...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adapti...
In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adapti...
Summary: We consider estimating a probability density p based on a random sample from this density b...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider estimating a probability density p based on a random sample from this density by a Bayes...
We consider estimating a probability density p based on a random sample from this density by a Bayes...
We consider estimating a probability density p based on a random sample from this density by a Bayes...
International audienceWe derive rates of contraction of posterior distributions on non-parametric mo...
International audienceWe derive rates of contraction of posterior distributions on non-parametric mo...
We derive rates of contraction of posterior distributions on nonparametric models resulting from sie...
working paper : http://arxiv.org/abs/1204.2392v2We derive rates of contraction of posterior distribu...
We consider the problem of estimating the mean of an infinite-dimensional normal distribution from t...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...
We consider the problem of estimating the mean of an infinite-break dimensional normal distribution ...