Variational Autoencoders for Efficient Simulation-Based Inference

• URL: http://arxiv.org/abs/2411.14511v1
• Date: Thu, 21 Nov 2024 12:24:13 GMT
• Title: Variational Autoencoders for Efficient Simulation-Based Inference
• Authors: Mayank Nautiyal, Andrey Shternshis, Andreas Hellander, Prashant Singh,
• Abstract summary: We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. We demonstrate the efficacy of these models on well-established benchmark problems, achieving results comparable to flow-based approaches.
• Score: 0.3495246564946556
• License:
• Abstract: We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently estimate complex posterior distributions arising from stochastic simulations. We explore two variations of this approach distinguished by their treatment of the prior distribution. The first model adapts the prior based on observed data using a multivariate prior network, enhancing generalization across various posterior queries. In contrast, the second model utilizes a standard Gaussian prior, offering simplicity while still effectively capturing complex posterior distributions. We demonstrate the efficacy of these models on well-established benchmark problems, achieving results comparable to flow-based approaches while maintaining computational efficiency and scalability.

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