Say you're trying to maximize a likelihood \(p_{\theta}(x)\), but you only have an unnormalized version \(\hat{p_{\theta}}\) for which \(p_{\theta}(x) = \frac{\hat{p_\theta}(x)}{N_\theta}\). How do you pick \(\theta\)? Well, you can rely on the magic of self normalized importance sampling.

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