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Mastering Probabilistic Graphical Models Using Python by Abinash Panda, Ankur Ankan

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Collapsed importance sampling

In the case of full particles for importance sampling, we used to generate particles from another distribution, and then, to compensate for the difference, we used to associate a weighting to each particle. Similarly, in the case of collapsed particles, we will be generating particles for the variables Collapsed importance sampling and getting the following dataset:

Collapsed importance sampling

Here, the sample Collapsed importance sampling is generated from the distribution Q. Now, using this set of particles, ...

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