General Stochastic

General stochastic processes encompass the study of systems evolving randomly over time, aiming to model and predict their behavior. Current research focuses on developing efficient algorithms for analyzing these processes, including stochastic gradient descent (SGD) variants and neural network-based approaches like Neural Jump ODEs, often leveraging concepts from Markov chains and point processes. These advancements are improving the accuracy and efficiency of Bayesian inference for complex models and finding applications in diverse fields such as real-time bidding, decentralized optimization, and financial modeling. The ability to effectively model and analyze general stochastic systems is crucial for advancing understanding and prediction in numerous scientific and engineering domains.