DeepAI AI Chat
Log In Sign Up

Bayesian Learning for Dynamic Inference

by   Aolin Xu, et al.

The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.


page 1

page 2

page 3

page 4


Dynamic Inference

Traditional statistical estimation, or statistical inference in general,...

Scalable Bayesian Inverse Reinforcement Learning

Bayesian inference over the reward presents an ideal solution to the ill...

An Information-Theoretic Analysis of Bayesian Reinforcement Learning

Building on the framework introduced by Xu and Raginksy [1] for supervis...

Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

Bayesian reward learning from demonstrations enables rigorous safety and...

An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

In [1, 2], we have explored the theoretical aspects of feature extractio...

Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components

Bayesian statistical inference loses predictive optimality when generati...