Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries
arXiv:2606.04324v1 Announce Type: new Abstract: One of the primary challenges in Bayesian inference on the parameters of a diffusion model from discrete observations is the unavailability of an analytical expression for the transition density function between consecutive observation times, which is needed to derive the likelihood function. Extending previous studies that solve Fokker-Planck (FP) type partial differential equations with Normalizing Flows, we propose a new Normalizing Flow architecture to learn the transition density function of the diffusion process between two observation time
The continuous advancements in AI and machine learning techniques, particularly in Normalizing Flows and neural networks, enable new approaches to complex probabilistic inference problems.
Improved Bayesian inference for diffusion models can enhance predictive accuracy and decision-making in fields ranging from finance and climate modeling to drug discovery and robotics, where understanding complex systems with discrete observations is critical.
This research introduces a novel Normalizing Flow architecture that allows for more accurate and computationally efficient inference of transition densities in diffusion processes, making previously intractable problems more solvable.
- · AI/ML researchers
- · Financial modeling firms
- · Robotics developers
- · Drug discovery and biotech
- · Traditional inference methods
- · Sectors reliant on less accurate predictive models
More sophisticated and reliable AI models become feasible for real-world applications requiring nuanced understanding of dynamic systems.
Accelerated progress in scientific discovery and autonomous systems due to the ability to better model and predict chaotic or complex phenomena.
New classes of AI-driven products and services emerge that leverage enhanced probabilistic modeling for forecasting and control, potentially disrupting existing industries where uncertainty is a significant factor.
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Read at arXiv cs.LG