Sequential updating is a process in Bayesian statistics where prior beliefs are revised in light of new evidence, allowing for an ongoing refinement of beliefs. This method enables the integration of incoming data to continuously adjust probabilities, ensuring that our understanding evolves with each new piece of information. It reflects the dynamic nature of knowledge, highlighting how initial assumptions can be modified as more data becomes available.
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