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Aleatory Uncertainty & Epistemic Uncertainty
So now you are able to distinguish SNN and BNN and know the difference between them. As mentioned, BNN is used to measure the uncertainties of the model. In fact, there are two types of uncertainties.
Aleatoric uncertainty is also known as statistical uncertainty. In Statistics, it is representative of unknowns that differ each time we run the same experiment (train the model). In deep learning, aleatory uncertainty refers to the uncertainty of the model weights. As shown in the below chart, every time we train the model, the weights may slightly vary. This variation is actually the aleatory uncertainty.
Epistemic uncertainty is also known as systematic uncertainty. In deep learning, it refers to the uncertainty of the model outputs. As shown in the below chart, given that the black line is the prediction and the orange area would be the epistemic uncertainty. You can regard it as the confidence level of the prediction. In the other words, it tells you how confident your prediction result is. If the interval is small, the actual value would have a larger chance to have a closer value towards your prediction value. In the contrary, if the interval is large, the actual value may have a big discrepancy with your prediction value.
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