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  • Causal network  (1)
  • population pharmacokinetics  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Estimation of population pharmacokinetics using the Gibbs sampler (1995)
    Springer
    Journal of pharmacokinetics and pharmacodynamics 23 (1995), S. 407-435 
    Details
    ISSN: 1573-8744
    Keywords: Gibbs sampling ; population pharmacokinetics ; gentamicin
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02353641
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Techniques for Bayesian analysis in expert systems (1990)
    Springer
    Annals of mathematics and artificial intelligence 2 (1990), S. 353-366 
    Details
    ISSN: 1573-7470
    Keywords: Causal network ; imprecise probability ; beta distribution ; unsupervised learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A causal network is frequently used as a representation for qualitative medical knowledge, in which conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. For probabilities temporarily assumed known, we describe efficient algorithms for propagating the effects of multiple items of evidence around multiply-connected networks and hence providing precise probabilistic revision of beliefs concerning the current patient. As a database accumulates we also require the quantitative aspects of the model to be updated, as well as to learn about the qualitative structure, and we suggest some formal statistical tools for these problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01531016
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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