The content discusses a novel approach to Bayesian inference using structured mixtures of probability distributions. The proposed method, SeMPLE, outperforms neural network-based methods in accuracy and efficiency across various benchmark models.
Simulation-based inference (SBI) bypasses the need for a likelihood function by considering a generative model or simulator. Recent methods have used neural networks to approximate likelihoods and posterior distributions. SeMPLE utilizes Gaussian Locally Linear Mapping (GLLiM) to provide accurate approximations for both likelihood and posterior distribution simultaneously. By incorporating an Expectation-Maximization algorithm within GLLiM, SeMPLE offers efficient posterior sampling in a tuning-free Metropolis-Hastings sampler. The method demonstrates superior performance in accuracy and resource efficiency compared to state-of-the-art neural network-based approaches like SNL and SNPE-C.
SeMPLE's resource requirements are significantly lower than traditional methods like SNL and SNPE-C, making it an efficient choice for Bayesian inference tasks. The approach leverages the power of structured mixtures of probability distributions to provide accurate posterior inference with minimal computational footprint. Overall, SeMPLE offers a frugal strategy that balances accuracy and computational efficiency in Bayesian inference tasks.
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