New approach in 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 structural parameters, i.e., the arc weights of SSMs from data and exploiting the reliability of detected inputs. Its convergence behavior is analyzed and an expression for the error at steady state is derived. Simulations assess the performance of the proposed and existing algorithms for a gene regulatory network.

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