Recursive Bayesian Estimation

Recursive Bayesian estimation is a statistical approach that updates the probability distribution of a system's state as new evidence or data becomes available. It uses Bayes' theorem to combine prior knowledge about the system with new observations to arrive at a more accurate posterior estimate.

This process is inherently recursive, meaning the current estimate becomes the prior for the next time step. In the context of cryptocurrency, this allows models to constantly learn and refine their understanding of market states without needing to reprocess the entire history of data.

It is the fundamental logic behind many advanced tracking and filtering algorithms used in quantitative finance. This method is highly effective at handling uncertainty and evolving market conditions.

It ensures that the model remains relevant and responsive to the most recent information flow. It is widely used in adaptive trading strategies that must adjust to rapidly changing liquidity conditions.