Title :
Bayesian field theory: nonparametric approaches to density estimation
Author_Institution :
Inst. fur Theor. Phys. I., Munster Univ., Germany
Abstract :
Nonparametric approaches to density estimation are discussed from a Bayesian perspective. Being in general nonGaussian the resulting models have to be solved by discretization. A numerical example shows that this can be computationally feasible for low-dimensional problems
Keywords :
Bayes methods; computational complexity; learning (artificial intelligence); neural nets; probability; Bayesian field theory; computational feasibility; discretization; learning; low-dimensional problems; neural net training; non-Gaussian models; nonGaussian models; nonparametric density estimation; Bayesian methods; Boundary conditions; Eigenvalues and eigenfunctions; Encoding; Gaussian processes; Lagrangian functions; Quantum mechanics;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857868
