SSMs are a useful modelling tool in systems biology and medicine. While models in these disciplines are traditionally hand-crafted, an automated generation based on experimental data becomes a topic of research interest. In particular, our goal was to classify measured processes using the generated models. An innovative likelihood-based adaptive learning approach capable of learning the […]
New approach in Likelihood-Based Adaptive Learning for Stochastic State-Based Models
