Large-scale Variational Gaussian State-Space Models with Efficient Inference

Konsep Inti

The author introduces a structured variational approximation and inference algorithm to simplify inference in state-space models, enabling efficient evaluation of the ELBO. By exploiting low-rank updates and sample approximations, the necessary statistics can be computed efficiently.

Abstrak

The content discusses the introduction of an amortized variational inference algorithm and structured variational approximation for state-space models with nonlinear dynamics driven by Gaussian noise. The proposed framework allows for efficient evaluation of the ELBO and low-variance stochastic gradient estimates without diagonal Gaussian approximations. The key points include the importance of understanding temporal structures through state-space models, challenges in unsupervised settings, and the benefits of variational autoencoder frameworks.

State-space models are crucial for understanding complex natural phenomena's temporal structure through their underlying dynamics. In unsupervised settings, learning system dynamics from observed data poses challenges that can be addressed using variational autoencoder frameworks. The proposed structured variational approximation simplifies inference in state-space models with nonlinear dynamics driven by Gaussian noise.

The content details how the proposed framework enables efficient evaluation of the ELBO and low-variance stochastic gradient estimates without resorting to diagonal Gaussian approximations. It highlights the significance of learning system dynamics from observed data in unsupervised settings using variational autoencoder frameworks.

Key metrics or figures used to support these arguments are not explicitly mentioned in this content.

Statistik

Overall time complexity: O(TL(Sr + S2 + r2))

Kutipan

Wawasan Utama Disaring Dari

Large-scale variational Gaussian state-space models

by Matthew Dowl... pada arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01371.pdf

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