  In this paper we present BayesLDM, a system for Bayesian longitudinal data
modeling consisting of a high-level modeling language with specific features
for modeling complex multivariate time series data coupled with a compiler that
can produce optimized probabilistic program code for performing inference in
the specified model. BayesLDM supports modeling of Bayesian network models with
a specific focus on the efficient, declarative specification of dynamic
Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification
with inspection of available data and outputs code for performing Bayesian
inference for unknown model parameters while simultaneously handling missing
data. These capabilities have the potential to significantly accelerate
iterative modeling workflows in domains that involve the analysis of complex
longitudinal data by abstracting away the process of producing computationally
efficient probabilistic inference code. We describe the BayesLDM system
components, evaluate the efficiency of representation and inference
optimizations and provide an illustrative example of the application of the
system to analyzing heterogeneous and partially observed mobile health data.
