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The EMBEr project aims to develop a new equivalence notion for probabilistic programming based on moment comparison. This approach will enhance the flexibility of model comparison, particularly in scenarios with incomplete information, advancing applications in Bayesian inference and cyber-physical systems.
Probabilistic programming has become an increasingly popular paradigm for representing probabilistic models due to the possibility of decoupling model representation and probabilistic inference.
As for any class of models, equivalence is a paramount property, as it allows for reduction and comparison.
However, the current notion of equivalence for probabilistic programs is very strict, relying on distributional equality and its weaker variants.
These existing notions of equivalence are particula…
TECHNISCHE UNIVERSITAET WIEN
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