#155 Probabilistic Programming for the Real World, with Andreas Munk

About this episode

The episode discusses the integration of deep learning and probabilistic programming with guest Andreas Munk.

Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work Takeaways: Q: Why is bridging deep learning and probabilistic programming so important? A: Deep learning is extraordinarily good at fitting complex functions, but it throws away uncertainty. Probabilistic programming keeps uncertainty explicit throughout. Combining the two – as in inference compilation – lets you get the expressiveness of neural networks while still doing proper Bayesian inference. Q: What is inference compilation and how does it relate to amortized inference? A: Amortized inference is the general idea of training a model upfront so you don't have to run expensive inference from scratch every single time. Inference compilation is a specific form of amortized inference where a neural network is trained to propose good posterior samples for a given probabilistic program – essentially learning to do inference rather than computing it fresh each query. Q: What is PyProb and what problems does it solve? A: PyProb is a probabilistic programming library…

People in this episode

Host: Alexandre Andorra

Guest: Andreas Munk

Topics covered

• probabilistic programming
• deep learning
• Bayesian inference
• amortized inference
• inference compilation
• scientific modeling

Keywords

• probabilistic programming
• deep learning
• Bayesian inference
• inference compilation
• PyProb
• amortized inference

Mentioned in this episode

Organizations: Patreon, PyProb

Products: Bayesian Modeling Course

Books & works: Good Bayesian

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