Summary of Eliminating Ratio Bias For Gradient-based Simulated Parameter Estimation, by Zehao Li and Yijie Peng
Eliminating Ratio Bias for Gradient-based Simulated Parameter Estimation
by Zehao Li, Yijie Peng
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 tackles the problem of parameter calibration in stochastic models where the likelihood function is not analytically available. The proposed gradient-based simulated parameter estimation framework uses a multi-time scale algorithm to address ratio bias in maximum likelihood estimation and posterior density estimation problems. The framework also includes a nested simulation optimization structure, with theoretical analyses on strong convergence, asymptotic normality, convergence rate, and budget allocation strategies. This approach is further extended to neural network training, providing a novel perspective on stochastic approximation in machine learning. Numerical experiments demonstrate that the algorithm can improve estimation accuracy and reduce computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a tricky problem in math where we don’t have a simple way to calculate some important numbers. The scientists came up with a new method using computers to find these numbers, which is better than previous methods. They also showed how their approach works well for training artificial intelligence models like neural networks. By testing their idea on computer simulations, they found that it can give more accurate results and save time. |
Keywords
» Artificial intelligence » Density estimation » Likelihood » Machine learning » Neural network » Optimization
