In this paper, we present a direct fusion algorithm for processing the combination of two Dirac mixture densities. The proposed approach allows the multiplication of two Dirac mixture densities without requiring identical support and thus enables the fusion of two independently generated sample sets. The resulting posterior Dirac mixture density is an approximation of the true continuous density that would result from the processing of the underlying true continuous density functions. This procedure is based on a suboptimal greedy approximation of the joint state space by means of a Dirac mixture that iteratively increases the resolution of the fusion result while considering only the relevant regions in the joint state space, where the fus...
Abstract — This paper addresses the challenges of the fusion of two random vectors with imprecisely ...
Undirected cycles in Bayesian networks are often treated by using clustering methods. This results i...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
Nonlinear fusion of multi-dimensional densities is an important application in Bayesian state estima...
Abstract – Nonlinear fusion of multi-dimensional random variables is an important application of Bay...
Greedy procedures for suboptimal Dirac mixture approximation of an arbitrary probability density fun...
Abstract — A deterministic procedure for optimal approximation of arbitrary probability density func...
For the optimal approximation of multivariate Gaussian densities by means of Dirac mixtures, i.e., b...
The sample-based recursive prediction of discrete-time nonlinear stochastic dynamic systems requires...
Since the advent of Monte-Carlo particle filtering, particle representations of densities have becom...
This paper introduces a new approach to the recursive propagation of probability density functions t...
Abstract — This paper proposes a systematic procedure for approximating arbitrary probability densit...
This paper presents a filter approach for estimating the state of nonlinear dynamic systems based on...
In this paper, we present a novel approach to parametric density estimation from given samples. The ...
This paper proposes a systematic procedure for approximating arbitrary probability density functions...
Abstract — This paper addresses the challenges of the fusion of two random vectors with imprecisely ...
Undirected cycles in Bayesian networks are often treated by using clustering methods. This results i...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
Nonlinear fusion of multi-dimensional densities is an important application in Bayesian state estima...
Abstract – Nonlinear fusion of multi-dimensional random variables is an important application of Bay...
Greedy procedures for suboptimal Dirac mixture approximation of an arbitrary probability density fun...
Abstract — A deterministic procedure for optimal approximation of arbitrary probability density func...
For the optimal approximation of multivariate Gaussian densities by means of Dirac mixtures, i.e., b...
The sample-based recursive prediction of discrete-time nonlinear stochastic dynamic systems requires...
Since the advent of Monte-Carlo particle filtering, particle representations of densities have becom...
This paper introduces a new approach to the recursive propagation of probability density functions t...
Abstract — This paper proposes a systematic procedure for approximating arbitrary probability densit...
This paper presents a filter approach for estimating the state of nonlinear dynamic systems based on...
In this paper, we present a novel approach to parametric density estimation from given samples. The ...
This paper proposes a systematic procedure for approximating arbitrary probability density functions...
Abstract — This paper addresses the challenges of the fusion of two random vectors with imprecisely ...
Undirected cycles in Bayesian networks are often treated by using clustering methods. This results i...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...