Gradient estimators for parameter inference in discrete stochastic kinetic models
Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For deterministic models, parameter inference often relies on gradients, which can be obtained efficiently through automatic differentiation (AD). However, AD cannot be applied directly to the Gillespie stochastic simulation algorithm (SSA), since sampling from a discrete set of reactions introduces non-differentiable operations. In this work, we adopt three gradient estimators from machine learning for the Gillespie SSA: the Gumbel-Softmax Straight-Through (GS-ST) estimator, the Score Function estimator, and the Alternative Path estimator. We use the estimators ...
| Authors | Ludwig Burger, Annalena Kofler, Lukas Heinrich et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 2 April 2026 |
| Last updated at source | 3 June 2026 |
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