Detecting Parameter Symmetries in Probabilistic Models

Probabilistic models play a central role in modern machine learning. They offer a powerful framework for learning from data, and they have found applications in a variety of scientific fields beyond machine learning. A longstanding goal in machine learning and statistics is to achieve a separation between modeling and inference, so that users of these tools may focus on specifying models without having to implement new inference algorithms every time the models change. Recently, work in probabilistic programming has taken up this challenge, seeking to unify probabilistic modeling with computer programming in order to dramatically increase the effectiveness of machine learning experts (DARPA, 2013) and to equip non-experts with effective tools for specifying models and performing inference. We anticipate that continued success toward these goals will decrease the reliance of machine learning practitioners on tried-and-true models and will shift the community toward a paradigm grounded in flexible tools for rapidly prototyping and designing new models (Bishop, 2013).