Visualizing the Consequences of Evidence in Bayesian Networks

2017-07-04 · Clifford Champion, Charles Elkan

This paper addresses the challenge of viewing and navigating Bayesian networks as their structural size and complexity grow. Starting with a review of the state of the art of visualizing Bayesian networks, an area which has largely been passed over, we improve upon existing visualizations in three ways. First, we apply a disciplined approach to the graphic design of the basic elements of the Bayesian network. Second, we propose a technique for direct, visual comparison of posterior distributions resulting from alternative evidence sets. Third, we leverage a central mathematical tool in information theory, to assist the user in finding variables of interest in the network, and to reduce visual complexity where unimportant. We present our methods applied to two modestly large Bayesian networks constructed from real-world data sets. Results suggest the new techniques can be a useful tool for discovering information flow phenomena, and also for qualitative comparisons of different evidence configurations, especially in large probabilistic networks.

📄 PDF Abstract BibTeX arXiv:1707.00791

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On the Expressiveness of Approximate Inference in Bayesian Neural Networks

2019-09-02 · NeurIPS 2020 12 · Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner

While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood. We study the quality of comm…

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PyVBMC: Efficient Bayesian inference in Python

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PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020). VBMC is an approximate inference metho…

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We propose to quantify dependence between two systems $X$ and $Y$ in a dataset $D$ based on the Bayesian comparison of two models: one, $H_0$, of statistical independence and another one, $H_1$, of dependence. In this fr…

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

2017-02-15 · Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling

This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that …

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Information Geometry of Variational Bayes

2025-09-19 · Mohammad Emtiyaz Khan arxiv

We highlight a fundamental connection between information geometry and variational Bayes (VB) and discuss its consequences for machine learning. Under certain conditions, a VB solution always requires estimation or compu…