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Bayes' Rule Using Imprecise Probabilities [Lecture Notes]
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DOI：10.1109/MSP.2023.3335893.png)
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Abstract
En 中文
Bayes' rule, as one of the fundamental concepts of statistical signal processing, provides a way to update our belief about an event based on the arrival of new pieces of evidence. Uncertainty is traditionally modeled by a probability distribution. Prior belief is thus expressed by a prior probability distribution, while the update involves the likelihood function, a probabilistic expression of how likely it is to observe the evidence. It has been argued by many statisticians, however, that a broadening of probability theory is required because one may not always be able to provide a probability for every event, due to the scarcity of training data.
Keywords:
Uncertainty
Training data
Signal processing
Probabilistic logic
Probability distribution
Bayes methods
Statistical analysis
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