vsimkus/torch-reparametrised-mixture-distribution
PyTorch implementation of the mixture distribution family with implicit reparametrisation gradients.
This project helps machine learning practitioners or researchers working with complex data by providing a way to model data using a combination of simpler probability distributions. It takes in your data and helps you understand its underlying structure, even when that structure is a blend of different patterns. It's designed for those doing advanced statistical modeling or Bayesian inference.
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Use this if you need to perform variational inference with models that describe your data as a blend of several different, simpler probability patterns.
Not ideal if your data is multivariate and you expect the individual components of your mixture model to have complex, non-factorized relationships.
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Jan 22, 2024
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