Summary of Neural Surrogate Hmc: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood, by Linnea M Wolniewicz et al.
Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood
by Linnea M Wolniewicz, Peter Sadowski, Claudio Corti
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Astrophysical Phenomena (astro-ph.HE)
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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 paper proposes an innovative method for efficiently computing likelihood functions in Bayesian inference with Markov Chain Monte Carlo. The approach, dubbed “surrogate likelihood,” leverages neural networks to approximate complex likelihood functions derived from partial differential equations (PDEs). By amortizing the computation of these PDE-based likelihoods, the authors demonstrate significant speedups and reduced noise in likelihood evaluations. Furthermore, the surrogate likelihood enables fast gradient calculations, facilitating efficient posterior sampling. The proposed method is applied to a model of heliospheric transport, showcasing its effectiveness in solving complex Bayesian inference problems. Key techniques include amortized computation, neural network-based likelihood approximation, and PDE-based likelihood calculation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out the probability of something happening based on some data. This can be a tricky problem, especially when the data is really complicated. Researchers have come up with a new way to make this process faster and more accurate. They use special computer programs called neural networks to estimate the probability, which makes it much quicker and easier to do. This approach also helps reduce errors in the calculations. The scientists tested their method on a problem involving space weather, where they were able to efficiently calculate the likelihood of certain events happening. |
Keywords
* Artificial intelligence * Bayesian inference * Likelihood * Neural network * Probability
