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Multi-Model Probabilistic Programming
The broader challenge of probabilistic modeling is in understanding and navigating spaces of alternative models.
Transforming Probabilistic Programs for Model Checking
Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying about the complexities of how to fit the models.
Static Analysis for Probabilistic Programs
We conclude that, while current static analysis techniques for probabilistic programs have practical limitations, there are a number of future directions with high potential to improve the state of statistical machine learning.
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