New bounds established for identifying parameters in interventional SDEs

Researchers have developed new theoretical bounds for the unique recovery of parameters in stochastic differential equations (SDEs) when subjected to multiple interventions. This work provides the first provable bounds for recovering SDE parameters from their stationary distributions, offering tight bounds for linear SDEs and upper bounds for nonlinear SDEs under specific conditions. The findings were experimentally validated using synthetic data and applied to model gene regulatory dynamics, demonstrating the advantage of parameterizations with learnable activation functions. AI

IMPACT Provides theoretical foundations that could inform future AI models for dynamic systems analysis.

RANK_REASON Academic paper on a theoretical topic within machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Aaron Zweig
• Stochastic Differential Equations

• paper
• other

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