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Compressing Bayesian Inference with Information Maximization

Stefan Radev
Rensselaer Polytechnic Institute ~ Cognitive Science

Amortized deep learning methods are transforming the field of simulation-based inference (SBI). However, most amortized methods rely solely on simulated data to refine their global approximations. We investigate a method to jointly compress both simulated and actually observed exchangeable sequences with varying sizes and use the compressed representations for downstream Bayesian tasks. We employ information maximizing variational autoencoders (VAEs) which we augment with normalizing flows for more expressive representation learning. We showcase the ability of our method to learn informative embeddings on toy examples and two real world modeling scenarios.



Deep learning
simulation-based inference
Bayesian estimation
de Finetti
model comparison

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Cite this as:

Radev, S. T. (2023, July). Compressing Bayesian Inference with Information Maximization. Abstract published at MathPsych/ICCM/EMPG 2023. Via