Pages that link to "Item:Q736434"
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The following pages link to Fitting nonlinear ordinary differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm (Q736434):
Displaying 9 items.
- Modeling noisy data with differential equations using observed and expected matrices (Q603160) (← links)
- Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (Q725305) (← links)
- A joint process model of consensus and longitudinal dynamics (Q826872) (← links)
- Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data (Q902935) (← links)
- Computation for latent variable model estimation: a unified stochastic proximal framework (Q2103576) (← links)
- Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data (Q2331187) (← links)
- Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE (Q2925701) (← links)
- A one‐step method for modelling longitudinal data with differential equations (Q4627515) (← links)
- Estimation of nonlinear mixed‐effects continuous‐time models using the continuous‐discrete extended Kalman filter (Q6185840) (← links)
