Summary of Mixed Noise and Posterior Estimation with Conditional Deepgem, by Paul Hagemann et al.
Mixed Noise and Posterior Estimation with Conditional DeepGEM
by Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an)
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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 presents a novel algorithm for jointly estimating the posterior and noise parameters in Bayesian inverse problems using an expectation maximization (EM) approach. The method proposes learning a conditional normalizing flow that approximates the posterior based on current noise parameters, and then updates the noise parameters again using an EM algorithm with analytical formulas. This approach is shown to be effective at incorporating information from multiple measurements, unlike previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better estimate things we can’t directly measure by combining different types of noisy data. The researchers developed a new way to do this using something called an expectation maximization (EM) algorithm. They also showed that their method is good at using information from many measurements, which is helpful in lots of fields like nanotechnology. |
