Simulating Posterior Bayesian Neural Networks with Dependent Weights

• URL: http://arxiv.org/abs/2507.22095v1
• Date: Tue, 29 Jul 2025 15:54:34 GMT
• Title: Simulating Posterior Bayesian Neural Networks with Dependent Weights
• Authors: Nicola Apollonio, Giovanni Franzina, Giovanni Luca Torrisi,
• Abstract summary: We consider posterior Bayesian fully connected and feedforward deep neural networks with dependent weights.<n>We identify the distribution of the wide width limit and provide an algorithm to sample from the network.<n>All the theoretical results are numerically validated.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we consider posterior Bayesian fully connected and feedforward deep neural networks with dependent weights. Particularly, if the likelihood is Gaussian, we identify the distribution of the wide width limit and provide an algorithm to sample from the network. In the shallow case we explicitly compute the distribution of the output, proving that it is a Gaussian mixture. All the theoretical results are numerically validated.

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