Stochastic Process

Stochastic processes model systems evolving randomly over time, aiming to understand their underlying dynamics and make predictions. Current research emphasizes developing efficient algorithms for inference and parameter estimation in various stochastic process models, including Bayesian methods, diffusion processes, and neural network-based approaches like variational autoencoders and neural processes. These advancements are crucial for diverse applications, from analyzing financial time series and modeling biological systems to improving large language models and enhancing computer vision techniques. The field is also actively exploring the theoretical foundations of stochastic processes, particularly in high-dimensional and infinite-dimensional settings, and developing robust methods for causal inference in these systems.