We study a novel method for maximum a posteriori (MAP) estimation of the probability density function of an arbitrary, independent and identically distributed \(d\)-dimensional data set. We give an interpretation of the MAP algorithm in terms of regularised maximum likelihood. We also present numerical experiments using a sparse grid combination technique and the `opticom' method. The numerical results demonstrate the viability of parallelisation for the combination technique. References H. J. Bungartz, M. Griebel, D. Roschke and C. Zenger. Pointwise convergence of the combination technique for the Laplace equation. East-West J. Numer. Math, 2:21--45 (1994). http://zbmath.org/?q=an:00653220 J. Garcke. Regression with the optimi...
The dimensionality of current applications increases which makes the density estimation a difficult ...
When it is known a priori exactly to which finite dimensional manifold the probability density funct...
Let X_1, ..., X_n be independent and identically distributed random vectors with a log-concave (Lebe...
We study a novel method for maximum a posteriori (MAP) estimation of the probability density functio...
With the recent growth in volume and complexity of available data has come a renewed interest in the...
In real-world applications mathematical models often involve more than one variable. For example, a...
Density estimation is a classical and well studied problem in modern statistics. In the case of low ...
In this paper, we consider the problem of estimating a conditional density in moderately large dimen...
This paper studies the estimation of the conditional density f (x, ·) of Y i given X i = x, from the...
Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in m...
Nous considérons le problème d’estimation de densités conditionnelles en modérément grandes dim...
We adopt a maximum a posteriori (MAP) estimation based approach for recovering sparse signals from a...
Was previously entitled "Compressible priors for high-dimensional statistics"International audienceW...
International audienceWe present a study of density estimation, the conversion of discrete particle ...
A generalized or tunable-kernel model is proposed for probability density function estimation based ...
The dimensionality of current applications increases which makes the density estimation a difficult ...
When it is known a priori exactly to which finite dimensional manifold the probability density funct...
Let X_1, ..., X_n be independent and identically distributed random vectors with a log-concave (Lebe...
We study a novel method for maximum a posteriori (MAP) estimation of the probability density functio...
With the recent growth in volume and complexity of available data has come a renewed interest in the...
In real-world applications mathematical models often involve more than one variable. For example, a...
Density estimation is a classical and well studied problem in modern statistics. In the case of low ...
In this paper, we consider the problem of estimating a conditional density in moderately large dimen...
This paper studies the estimation of the conditional density f (x, ·) of Y i given X i = x, from the...
Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in m...
Nous considérons le problème d’estimation de densités conditionnelles en modérément grandes dim...
We adopt a maximum a posteriori (MAP) estimation based approach for recovering sparse signals from a...
Was previously entitled "Compressible priors for high-dimensional statistics"International audienceW...
International audienceWe present a study of density estimation, the conversion of discrete particle ...
A generalized or tunable-kernel model is proposed for probability density function estimation based ...
The dimensionality of current applications increases which makes the density estimation a difficult ...
When it is known a priori exactly to which finite dimensional manifold the probability density funct...
Let X_1, ..., X_n be independent and identically distributed random vectors with a log-concave (Lebe...