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It's much easier to deal with logarithms, as the relevant numbers are usually very small or very large. If you have a long exponential expression, it's hard to see the difference, but if you're looking at 4.3 vs 5.6, you can immediately see what's happening. And logarithms are a well-known (and well-understood) way of achieving this compression. You can ...


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I would like to add details to Oliver's answer. From the book "Pattern Recognition and Machine Learning" by Bishop (Section 1.2.5): In practice, it is more convenient to maximize the log of the likelihood function. Because the logarithm is monotonically increasing function of its argument, maximization of the log of a function is equivalent to ...


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