ADAPTIVE BAYESIAN ESTIMATION OF CONDITIONAL DENSITIES

Estimation of Face Depths by Conditional Densities

The expected value of missing data in a sample taken from a multivariate normal probability distribution is the mean of the conditional distribution of the missing dimensions given the known dimensions. We explain the derivation of this result, demonstrate its application to face image processing, then use it in a new method for recovering shape from image data. The context of our work is the u...

A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities

This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density is described through the Dirichlet process. In the mixture model, a kernel is used leading to a dynamic nonlinear autoregressivemodel. This model can approximate any linear autoregressivemodel arbitrarily closely while ...

A Neural Bayesian Estimator for Conditional Probability Densities

This article describes a robust algorithm to estimate a conditional probability density f(t|~x) as a non-parametric smooth regression function. It is based on a neural network and the Bayesian interpretation of the network output as a posteriori probabability. The network is trained using example events from history or simulation, which define the underlying probability density f(t, ~x). Once t...

Adaptive pointwise estimation of conditional density function

On Adaptive Wavelet Estimation of a Class of Weighted Densities

We investigate the estimation of a weighted density taking the form g = w(F )f , where f denotes an unknown density, F the associated distribution function and w is a known non-negative weight. Such a class encompasses many examples, including those arising in order statistics or when g is related to the maximum or the minimum of N (random or fixed) independent and identically distributed (i.i....