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So that's the idea of here climbing. And in the EAM algorithm, the way we achieve this is to do two things. First, we'll fix a lower bound of likelihood function. So this is the lower bound you can see here. And once we fit the lower bound, we can then maximize the lower bound. 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So we know our current gas is here. And by maximizing the lower bound, we'll move this point to the top, to here. And we can then map to the original liglow function we find at this point. Because it's a lower bound, we are guaranteed to improve this gas. 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In our case, this is the distribution that has been used to generate the work. In the end step then, we would exploit such augmented data, which would make it easier to estimate the distribution to improve the estimate of parameters. Here, improve is guaranteed in terms of the likelihood function. 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There are some properties that have to be satisfied in order for the parameters also to converge to some stable value. Now here, data augmentation is done probabilistically. 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