Summary of Hamiltonian Monte Carlo Inference Of Marginalized Linear Mixed-effects Models, by Jinlin Lai et al.
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
by Jinlin Lai, Justin Domke, Daniel Sheldon
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A probabilistic programming language can simplify Bayesian reasoning in Linear Mixed-Effects Models (LMMs), leveraging Markov Chain Monte Carlo (MCMC) or Hamiltonian Monte Carlo (HMC). However, manual marginalization of random effects can be challenging. This paper develops an algorithm to efficiently marginalize random effects in LMMs, reducing running time from cubic to linear using fast linear algebra techniques. The proposed method is beneficial for various models, including those from cognitive sciences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian reasoning in Linear Mixed-Effects Models (LMMs) can be tricky! Scientists often use special programming languages and sampling techniques like Markov Chain Monte Carlo (MCMC). But did you know that some LMMs have extra variables that make inference more efficient? This paper makes it easier to remove those unnecessary variables, making the process faster and more accurate. |
Keywords
* Artificial intelligence * Inference
