# Amortized Inference of Variational Bounds for Learning Noisy-OR

## TL;DR

This paper introduces Amortized Conjugate Posterior (ACP), a hybrid inference method combining classical and modern variational techniques, applied to noisy-OR models, improving inference accuracy and efficiency.

## Contribution

The paper proposes ACP, a novel hybrid inference approach that leverages classical conjugate priors with amortized inference, enhancing posterior approximation in noisy-OR models.

## Key findings

- ACP outperforms classical methods in accuracy.
- ACP matches or exceeds modern amortized inference.
- The approach is effective for noisy-OR models.

## Abstract

Classical approaches for approximate inference depend on cleverly designed variational distributions and bounds. Modern approaches employ amortized variational inference, which uses a neural network to approximate any posterior without leveraging the structures of the generative models. In this paper, we propose Amortized Conjugate Posterior (ACP), a hybrid approach taking advantages of both types of approaches. Specifically, we use the classical methods to derive specific forms of posterior distributions and then learn the variational parameters using amortized inference. We study the effectiveness of the proposed approach on the noisy-or model and compare to both the classical and the modern approaches for approximate inference and parameter learning. Our results show that the proposed method outperforms or are at par with other approaches.