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1. Continuous data models‏‎ (8 links)
2. Introduction to PK modeling using MLXPlore - Part I‏‎ (8 links)
3. Modeling the individual parameters‏‎ (8 links)
4. Modeling the observations‏‎ (8 links)
5. The SAEM algorithm for estimating population parameters‏‎ (8 links)
6. Models for count data‏‎ (7 links)
7. Model for categorical data‏‎ (7 links)
8. What is a model? A joint probability distribution!‏‎ (7 links)
9. Models for time-to-event data‏‎ (6 links)
10. Stochastic differential equations based models‏‎ (6 links)
11. The Metropolis-Hastings algorithm for simulating the individual parameters‏‎ (6 links)
12. Joint models‏‎ (6 links)
13. The individual approach‏‎ (5 links)
14. Estimation of the observed Fisher information matrix‏‎ (5 links)
15. Extension to multivariate distributions‏‎ (5 links)
16. Model with covariates‏‎ (5 links)
17. Template:OutlineTextL‏‎ (5 links)
18. Gaussian models‏‎ (5 links)
19. Hidden Markov models‏‎ (5 links)
20. Mixture models‏‎ (5 links)

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