Abstract
The BoolFilter package provides tools for state estimation and inference of partially-observed Boolean dynamical systems. This package contains functions for simulating data, obtaining the optimal estimator in the presence of various observation noise models, applying the optimal MMSE filter and smoother, particle filter implementation of optimal filter, as well as means of parameter estimation and network inference. BoolFilter was created explicitly to handle the latest tools developed for signal model of partially-observed Boolean dynamical system (POBDS). This signal model was introduced to model dynamical systems containing Boolean variables observed indirectly through a noisy measurement process. This observation noise could be a result of instrumentation, incorrect thresholding, etc
Levi D. McClenny
Graduate Research Fellow
My research interests are all things deep learning, especially artificial intelligence with applications in materials systems