smoothing in variance

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smoothing in variance

Post by jackycwwang »

Hi LP,

In 4. Naive Bayes Handwritten and 5. Naive Bayes in Code with MNIST, I understand the concept the add-one smoothing in p(X|C) = count(X, C) + 1 / count(C) + V, in the discrete dataset.

I know we can use the continuous Gaussian function to approximate p(X|C). The part that confused me is how to use/reason the smoothing technique from discrete probability in the continuous probability? I saw in lecture 5, you added smoothing to the varariance of each random variable. Why added here?

Thank you for your time!
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Re: smoothing in variance

Post by lazyprogrammer »

Thanks for your inquiry.

As you recall, MNIST is an image dataset. Some pixels have a constant value of 0.

In that case, the variance is 0. If the variance is 0 the PDF goes to infinity, which cannot be used for computations.
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