About this Research Topic
Bayesian inference has had a pretty wide range of applications in AI, including Bayesian networks, Bayesian spatial-temporal models, Bayesian inference and learning from neural networks and deep learning, Bayesian meta-learning, Bayesian reinforcement learning, Bayesian supervised learning, semi-supervised learning, and unsupervised learning. It has also been used to solve real-world problems, for example, Bayesian network for social media data and Bayesian decoding of brain images. However, there is quite a large space to fill in integrating Bayesian within the AI framework. For, example, there are still tremendous unsolved questions: how to quantify prior knowledge, how to justify probability distribution behind the likelihood of the parameters, how to implement Bayesian inference efficiently from posterior distributions, how to measure the effect of minor perturbation to prior and data, and how to improve the convergence rate of the algorithm in implementing Bayesian learning.
We call for applications of Bayesian concepts in AI under the statistical framework and the related Bayesian theory. It includes (but not limited to) the following areas:
• Recent developments in Bayesian theory and its methodology;
• Applications of Bayesian methods in AI;
• Computing technology in Bayesian inference, algorithms and its implementation;
• Applying Bayesian approach and AI techniques on solving real-world problems;
• Robust of Bayesian learning and Bayesian sensitive analysis in AI.
Keywords: Bayesian Inference, Neural Networks, Deep Learning, Supervised Learning, Bayesian Network, Bayesian theory, Bayesian learning
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