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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2
Bayesian Field Theory: Nonparametric Approaches to Density Estimation
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
| ASCII Text | x | ||
| Jörg C. Lemm, "Bayesian Field Theory: Nonparametric Approaches to Density Estimation," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 2, pp. 2018, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2, 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/IJCNN.2000.857868, author = {Jörg C. Lemm}, title = {Bayesian Field Theory: Nonparametric Approaches to Density Estimation}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {2}, year = {2000}, issn = {1098-7576}, pages = {2018}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857868}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Bayesian Field Theory: Nonparametric Approaches to Density Estimation SN - 1098-7576 SP EP A1 - Jörg C. Lemm, PY - 2000 VL - 2 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER - | |||
Nonparametric approaches to density estimation are discussed from a Bayesian perspective. Being in general non-Gaussian the resulting models have to be solved by discretization. A numerical example shows that this can be computationally feasible for low-dimensional problems.
Citation:
Jörg C. Lemm, "Bayesian Field Theory: Nonparametric Approaches to Density Estimation," ijcnn, vol. 2, pp.2018, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2, 2000
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