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31. Simulation‏‎ (16:10, 5 June 2013)
32. Home page‏‎ (14:53, 7 June 2013)
33. Introduction and notation‏‎ (14:54, 7 June 2013)
34. Description, representation and implementation of a model‏‎ (10:09, 21 June 2013)
35. Overview‏‎ (10:34, 21 June 2013)
36. What is a model? A joint probability distribution!‏‎ (10:40, 21 June 2013)
37. The SAEM algorithm for estimating population parameters‏‎ (11:17, 21 June 2013)
38. Gaussian models‏‎ (12:39, 25 June 2013)
39. Model with covariates‏‎ (13:23, 25 June 2013)
40. Extension to multivariate distributions‏‎ (13:28, 25 June 2013)
41. Additional levels of variability‏‎ (13:37, 25 June 2013)
42. Modeling the observations‏‎ (13:46, 25 June 2013)
43. Continuous data models‏‎ (13:54, 25 June 2013)
44. Introduction to PK modeling using MLXPlore - Part I‏‎ (14:27, 25 June 2013)
45. Models for count data‏‎ (15:00, 25 June 2013)
46. Models for time-to-event data‏‎ (15:10, 25 June 2013)
47. Joint models‏‎ (15:19, 25 June 2013)
48. Extensions‏‎ (15:28, 25 June 2013)
49. Mixture models‏‎ (15:39, 25 June 2013)
50. Hidden Markov models‏‎ (15:44, 25 June 2013)

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