Summary of Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models, by Sohail Reddy et al.
Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models
by Sohail Reddy, Hillary Fairbanks
First submitted to arxiv on: 18 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. The conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors. Instead, this work offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples, thereby improving the acceptance of samples by the high-fidelity model. This technique is demonstrated on a standard benchmark inference problem in groundwater flow, where it accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make a computer program run faster when solving big problems using special math formulas. Usually, these formulas are very complicated and take a long time to compute. To speed things up, researchers use simpler models that are not as accurate but can give them an answer quickly. This new method combines the best of both worlds by using simple models for parts of the problem where accuracy doesn’t matter so much, and more complicated models where it does. The result is a program that runs faster without losing too much accuracy. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Machine learning
