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Towards Data Science·2d ago·5 min read
Three Samplers, One Posterior: NUTS vs. Emcee vs. Nested Sampling
TL;DR
NUTS, Emcee, and Nested Sampling were benchmarked against exact ground truth in a comparative study, revealing their limitations in performance. The findings highlight the challenges faced by these popular sampling methods in accurately estimating posterior distributions.
✦ Why It Matters
Evaluate your current sampling methods against these findings to improve your Bayesian inference accuracy.
Key Takeaways
- Consider using NumPyro for Bayesian inference tasks.
- Benchmark your sampling methods against ground truth for accuracy.
- Be aware of the limitations of NUTS and Emcee in high dimensions.
- Explore Nested Sampling for problems requiring convergence.
Full Summary
The study evaluates three sampling methods: No-U-Turn Sampler (NUTS), Emcee, and Nested Sampling, using the NumPyro framework and the dynesty library. These methods are designed to sample from complex posterior distributions in Bayesian inference.
The benchmarking involved comparing their outputs against exact ground truth values to assess their accuracy and efficiency. Results showed that all three methods struggled under certain conditions, with notable discrepancies in their estimates.
For instance, NUTS and Emcee exhibited significant bias in high-dimensional spaces, while Nested Sampling faced challenges with convergence. These findings underscore the importance of selecting appropriate sampling techniques based on the specific characteristics of the problem at hand.