Summary of Sample-efficient Neural Likelihood-free Bayesian Inference Of Implicit Hmms, by Sanmitra Ghosh and Paul J. Birrell and Daniela De Angelis
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
by Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 introduces novel likelihood-free inference methods based on neural conditional density estimation for latent variable models. The approach reduces simulation burdens compared to classical ABC methods. When applied to Hidden Markov Models (HMMs), these methods estimate model parameters without considering the joint distribution of parameters and hidden states, leading to inaccurate posterior predictive distributions. To address this issue, a sample-efficient likelihood-free method is proposed for estimating high-dimensional hidden states in implicit HMMs. Our approach learns the intractable posterior distribution using autoregressive-flow models that exploit Markov properties. Evaluations on implicit HMMs show that our method produces comparable results to computationally expensive SMC algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops new ways to understand complex systems without needing a lot of data. It shows how to use special math tools called neural networks to estimate the hidden patterns in these systems, like what’s happening inside a computer program or a human brain. The approach is faster and more efficient than older methods that require a lot of calculations. By using this new method, scientists can get better estimates of what’s going on inside complex systems, which can help us understand them better. |
Keywords
» Artificial intelligence » Autoregressive » Density estimation » Inference » Likelihood
