New approach in Likelihood-Based Adaptive Learning for Stochastic State-Based Models

Likelihood-Based Adaptive Learning for Stochastic State-Based Models

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 […]

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