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- Accueil
- Additional levels of variability
- Admin/Vector.css
- Admin/vector.css
- Animations & Videos
- Code
- Common.css
- Continuous data models
- Description, representation and implementation of a model
- Dynamical systems ODEs
- Dynamical systems driven by ODEs
- Estimation
- Estimation of the log-likelihood
- Estimation of the observed Fisher information matrix
- Extension to multivariate distributions
- Extensions
- Formula
- Gaussian models
- Hidden Markov models
- Home page
- Introduction
- Introduction & notation
- Introduction and notation
- Introduction to PK modeling using MLXPlore - Part I
- Introduction to PK modeling using MLXPlore - Part II
- Joint models
- LauraTest
- Methods
- Mixture models
- Model evaluation
- Model for categorical data
- Model with covariates
- Modeling the individual parameters
- Modeling the observations
- Models
- Models for count data
- Models for time-to-event data
- MyTest
- Overview
- Simulation
- Stochastic differential equations based models
- Test
- TestMarc1
- TestMarc2
- TestMarc3
- TestMarc4
- TestMarc5
- TestMarc6
- TestTable
- Test Video
- Test Video1
- Test balloons
- Testing A joint probability
- The Metropolis-Hastings algorithm for simulating the individual parameters
- The SAEM algorithm for estimating population parameters
- The individual approach
- Vector.css
- Visualization
- What is a model? A joint probability distribution!
