Probabilistic programming languages are systems in which stochastic modeling primitives are “first class” citizens. At a minimum, these environments provide a clean separation of probabilistic modeling, which captures our assumptions about the world and the hypothesis space, and inference, i.e. learning which hypotheses provide the best explanation of the observed data. Though still a subject of intense academic research, these systems have recently emerged in various open source projects and are now available for widespread evaluation and use.
This talk will provide a high-level introduction to the state of the probabilistic programming field, and give a feel for what it’s like to use these environments.
Questions that will be answered include:
Beau co-founded two startups based on probabilistic inference, the second of which was acquired by Salesforce in 2012. He knows works there as a product manager. He received his PhD in computational neuroscience from MIT in 2008.
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