Add to ORKGThe maximum a posteriori method is generalised for infinite dimensional problems and it is shown that in this case the problem can be reduced to a nonlinear variational problem. This is not a trivial generalisation as the probability density used for th
The large deviations principle for Gaussian measures in Banach space is given by the generalized Sch...
The marginal maximum a posteriori probability (MAP) estimation problem, which cal-culates the mode o...
This paper studies the optimal control problem for point processes with Gaussian white-noised observ...
Abstract—This paper proposes a novel probabilistic variational method with deterministic annealing f...
Maximum a posteriori and Bayes estimators are two common methods of point estimation in Bay...
| openaire: EC/H2020/757275 /EU//PANAMAThere is a growing interest in probabilistic numerical soluti...
A variational problem characterizing the density estimator defined by the maximum a posteriori metho...
In this paper we are interested in the distribution of the maximum, or the maximum of the absolute v...
Many machine learning problems deal with the estimation of conditional probabilities $p(y \mid x)$ f...
Uncertainty quantification requires efficient summarization of high- or even infinite-dimensional (i...
This paper deals with the problem of obtaining methods to compute the distribution of the maximum of...
The notion of a posteriori probability, often used in hypothesis testing in connection with problems...
In this paper, we extend the correspondence between Bayesian estimation and optimal smoothing in a R...
Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior dis...
Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior dis...
The large deviations principle for Gaussian measures in Banach space is given by the generalized Sch...
The marginal maximum a posteriori probability (MAP) estimation problem, which cal-culates the mode o...
This paper studies the optimal control problem for point processes with Gaussian white-noised observ...
Abstract—This paper proposes a novel probabilistic variational method with deterministic annealing f...
Maximum a posteriori and Bayes estimators are two common methods of point estimation in Bay...
| openaire: EC/H2020/757275 /EU//PANAMAThere is a growing interest in probabilistic numerical soluti...
A variational problem characterizing the density estimator defined by the maximum a posteriori metho...
In this paper we are interested in the distribution of the maximum, or the maximum of the absolute v...
Many machine learning problems deal with the estimation of conditional probabilities $p(y \mid x)$ f...
Uncertainty quantification requires efficient summarization of high- or even infinite-dimensional (i...
This paper deals with the problem of obtaining methods to compute the distribution of the maximum of...
The notion of a posteriori probability, often used in hypothesis testing in connection with problems...
In this paper, we extend the correspondence between Bayesian estimation and optimal smoothing in a R...
Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior dis...
Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior dis...
The large deviations principle for Gaussian measures in Banach space is given by the generalized Sch...
The marginal maximum a posteriori probability (MAP) estimation problem, which cal-culates the mode o...
This paper studies the optimal control problem for point processes with Gaussian white-noised observ...