A Hybrid NUTS-Gibbs Sampler with State Space Marginalization for Estimation of Dynamic Structural Equation Models with Binomial Outcomes
This paper presents a hybrid NUTS-Gibbs sampler for dynamic structural equation models with binomial outcomes. The Gibbs step handles Pólya-Gamma latent variables from a logit link, while the NUTS step uses a Kalman filter to marginalize over latent states. arXiv preprint.
Efficient Bayesian Estimation of Dynamic Structural Equation Models via State Space Marginalization
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.
Modeling Cycles, Trends and Time-Varying Effects in Dynamic Structural Equation Models with Regression Splines
This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. arXiv preprint currently under revision.