A Survey of Bayesian Decision Making
Abstract
analysis, machine learning, artificial intelligence and many application fields. It provides a probability based method to deal with uncertainty
and reflect new information by constantly updating the probability distribution. In the Bayesian decision-making framework, decision makers first set the prior probability distribution based on historical data or expert knowledge, and then update these distributions according to the
newly collected data by using Bayesian theorem to reflect the latest cognition of the current situation. The advantage of Bayesian decision is
that it can deal with uncertainty naturally, and can continuously improve the decision through continuous data collection and analysis. In addition, it can also deal with complex dependencies and uncertainty propagation, which is crucial for many practical application scenarios. Starting from the origin of Bayesian decision theory, this paper introduces its development process and research in detail, aiming to have a clear
understanding of Bayesian decision theory from a macro perspective; Then the application of Bayesian decision theory in different fields is
described; Finally, the development prospects and challenges in the future are introduced.
Keywords
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DOI: http://dx.doi.org/10.70711/frim.v3i3.6184
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