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    • Mixed-Effects Tutorial 1: Nonlinear Random-Effects Model Across Multiple Estimation Methods
    • Mixed-Effects Tutorial 2: ODE Model with Dosing Events (MCEM)
    • Mixed-Effects Tutorial 3: Neural Differential-Equation Components (SAEM)
    • Mixed-Effects Tutorial 4: SoftTree Differential-Equation Components (SAEM)
    • Mixed-Effects Tutorial 5: Seizure Counts with Poisson and NegativeBinomial Outcomes (MCEM)
    • Mixed-Effects Tutorial 6: Left-Censored Nonlinear Model (Laplace)
    • Mixed-Effects Tutorial 7: VI for Mixed Effects (Not Supported)
    • Fixed-Effects Tutorial 1: Nonlinear Longitudinal Model (MLE + MAP)
    • Fixed-Effects Tutorial 2: Variational Inference (VI)
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  • Mixed-Effects Tutorial 7: VI for Mixed Effects (Not Supported)
  • Mixed-Effects Tutorial 7: VI for Mixed Effects (Not Supported)
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Mixed-Effects VI

Warning

VI (variational inference) is not supported for models with random effects. Calling fit_model with NoLimits.VI on a mixed-effects model will throw an error.

For full Bayesian inference on mixed-effects models, use MCMC. For likelihood-based mixed-effects estimation, use Laplace, LaplaceMAP, MCEM, or SAEM.

For a variational inference tutorial on a fixed-effects model, see Fixed-Effects Tutorial 2: Variational Inference (VI).

« Mixed-Effects Tutorial 6: Left-Censored Nonlinear Model (Laplace)Fixed-Effects Tutorial 1: Nonlinear Longitudinal Model (MLE + MAP) »

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