Hamiltonian Monte Carlo

Efficient Bayesian Estimation of Dynamic Structural Equation Models via State Space Marginalization

Øystein Sørensen

This paper shows that the within-level part of any dynamic structural equation model can be reformulated as a linear Gaussian state space model, enabling analytical marginalization via a Kalman filter and highly efficient estimation via Hamiltonian Monte Carlo. arXiv preprint.

• DSEM
• intensive longitudinal data
• Kalman filter
• Hamiltonian Monte Carlo
• state space model