BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data
This system addresses the problem of cumbersome and inefficient modeling workflows for researchers and practitioners in domains like mobile health that involve complex longitudinal data, representing an incremental improvement by abstracting inference code generation.
The authors tackled the challenge of modeling complex multivariate time series data by introducing BayesLDM, a domain-specific language and compiler for Bayesian longitudinal data modeling, which accelerates iterative workflows by automating efficient probabilistic inference code generation, as demonstrated with mobile health data.
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.