Practical Issues in Constructing a Bayes' Belief Network
Bayes belief networks and influence diagrams are tools for constructing coherent probabilistic representations of uncertain knowledge. The process of constructing such a network to represent an expert's knowledge is used to illustrate a variety of techniques which can facilitate the process of structuring and quantifying uncertain relationships. These include some generalizations of the "noisy OR gate" concept. Sensitivity analysis of generic elements of Bayes' networks provides insight into when rough probability assessments are sufficient and when greater precision may be important.
Code (0)
등록된 구현이 없습니다.
Tasks
SensitivitySimilar Papers 제목 키워드 기반
Ergo: A Graphical Environment for Constructing Bayesian
We describe an environment that considerably simplifies the process of generating Bayesian belief networks. The system has been implemented on readily available, inexpensive hardware, and provides clarity and high perfor…
Probability Judgement in Artificial Intelligence
This paper is concerned with two theories of probability judgment: the Bayesian theory and the theory of belief functions. It illustrates these theories with some simple examples and discusses some of the issues that ari…
The Benefit of Being Bayesian in Online Conformal Prediction
Based on the framework of Conformal Prediction (CP), we study the online construction of valid confidence sets given a black-box machine learning model. By converting the target confidence levels into quantile levels, th…
Bayesian InferenceConformal PredictionPredictionExistence of Bayesian Equilibria in Incomplete Information Games without Common Priors
We consider incomplete information finite-player games where players may hold mutually inconsistent beliefs without a common prior. We introduce absolute continuity of beliefs, extending the classical notion of absolutel…
Learning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning
Meta-reinforcement learning requires utilizing prior task distribution information obtained during exploration to rapidly adapt to unknown tasks. The efficiency of an agent's exploration hinges on accurately identifying …
Meta Reinforcement LearningMuJoCo