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Volumn 38, Issue 1, 2005, Pages 295-300

Nonparametric identification of pharmacokinetic population models via Gaussian processes

Author keywords

Estimation theory; Neural networks; Nonparametric identification; Pharmacokinetic data; Regularization; Splines

Indexed keywords

BIOCHEMISTRY; NEURAL NETWORKS; RANDOM PROCESSES; SPLINES; STOCHASTIC SYSTEMS;

EID: 79960716354     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20050703-6-cz-1902.02164     Document Type: Conference Paper
Times cited : (6)

References (13)
  • 3
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • Girosi, F., M. Jones and T. Poggio (1995). Regularization theory and neural networks architectures. Neural Computation 7, 219-269.
    • (1995) Neural Computation , vol.7 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 6
    • 0001025418 scopus 로고
    • Bayesian interpolation
    • MacKay, D. J. C. (1992). Bayesian interpolation. Neural Computation 4, 415-447.
    • (1992) Neural Computation , vol.4 , pp. 415-447
    • MacKay, D.J.C.1
  • 7
    • 0038505133 scopus 로고    scopus 로고
    • Nonparametric AUC estimation in population studies with incomplete sampling: A Bayesian approach
    • Magni, P., R. Bellazzi, G. De Nicolao, I. Poggesi and M. Rocchetti (2002). Nonparametric AUC estimation in population studies with incomplete sampling: a Bayesian approach. J. Pharmacokin. Pharmacodyn. 29(5/6), 445- 471.
    • (2002) J. Pharmacokin. Pharmacodyn. , vol.29 , Issue.5-6 , pp. 445-471
    • Magni, P.1    Bellazzi, R.2    De Nicolao, G.3    Poggesi, I.4    Rocchetti, M.5
  • 8
    • 0031403082 scopus 로고    scopus 로고
    • A semiparametric method for describing noisy population pharmacokinetic data
    • Park, K., D. Verotta, T. F. Blaschke and L. B. Sheiner (1997). A semiparametric method for describing noisy population pharmacokinetic data. J. Pharmacokin. Biopharm. 25, 615- 642.
    • (1997) J. Pharmacokin. Biopharm. , vol.25 , pp. 615-642
    • Park, K.1    Verotta, D.2    Blaschke, T.F.3    Sheiner, L.B.4
  • 9
    • 0025490985 scopus 로고
    • Networks for approximation and learning
    • Poggio, T. and F. Girosi (1990). Networks for approximation and learning. Proc. IEEE 78, 1481-1497.
    • (1990) Proc. IEEE , vol.78 , pp. 1481-1497
    • Poggio, T.1    Girosi, F.2
  • 11
    • 4243839021 scopus 로고
    • Bayesian analysis of linear and nonlinear population models using the gibbs sampler
    • Wakefield, J., A. F. M. Smith, A. Racine-Poon and A. Gelfand (1994). Bayesian analysis of linear and nonlinear population models using the gibbs sampler. Appl. Statist. 41, 201-221.
    • (1994) Appl. Statist. , vol.41 , pp. 201-221
    • Wakefield, J.1    Smith, A.F.M.2    Racine-Poon, A.3    Gelfand, A.4
  • 12
    • 0030327432 scopus 로고    scopus 로고
    • The Bayesian modelling of covariates for population pharmacokinetic models
    • Wakefield, J. and J. Bennett (1996). The Bayesian modelling of covariates for population pharmacokinetic models. JASA 91, 917-927.
    • (1996) JASA , vol.91 , pp. 917-927
    • Wakefield, J.1    Bennett, J.2
  • 13
    • 0002295913 scopus 로고    scopus 로고
    • Gaussian processes for regression
    • (D. S. Touretzky, M. C. Mozer and M. E. Hasselmo, Eds.), Cambridge, MA: MIT Press
    • Williams, C. K. J. and C. E. Rasmussen (1996). Gaussian processes for regression. In: Advances in Neural Information Processing Systems (D. S. Touretzky, M. C. Mozer and M. E. Hasselmo, Eds.). Vol. 8. Cambridge, MA: MIT Press.
    • (1996) Advances in Neural Information Processing Systems , vol.8
    • Williams, C.K.J.1    Rasmussen, C.E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.