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

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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 designed specifically to support amortized inference workflows. It lets you write probabilistic models in Python and then train inference networks on top of them, making methods like inference compilation practical for real-world simulators and scientific models.

Full takeaways here.

Chapters:

00:00:00 Introduction to Bayesian Inference and Its Barriers 00:03:51 Andreas Munch's Journey into Statistics 00:10:09 Bridging the Gap: Bayesian Inference in Real-World Applications 00:15:56 Deep Learning Meets Probabilistic Programming 00:22:05 Understanding Inference Compilation and Amortized Inference 00:28:14 Exploring PyProb: A Tool for Amortized Inference 00:33:55 Probabilistic Surrogate Networks and Their Applications 00:38:10 Building Surrogate Models for Probabilistic Programming 00:45:44 The Challenge of Bayesian Inference in Enterprises 00:52:57 Communicating Uncertainty to Stakeholders 01:01:09 Democratizing Bayesian Inference with Evara 01:06:27 Insurance Pricing and Latent Variables 01:16:41 Modeling Uncertainty in Predictions 01:20:29 Dynamic Inference and Decision-Making 01:23:17 Updating Models with Actual Data 01:26:11 The Future of Bayesian Sampling in Excel 01:31:54 Navigating Business Challenges and Growth 01:36:40 Exploring Language Models and Their Applications 01:38:35 The Quest for Better Inference Algorithms 01:41:01 Dinner with Great Minds: A Thought Experiment

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