Exact Bayesian Inference Algorithm

An Exact Bayesian Inference Algorithm is a Bayesian Inference Algorithm that solves an exact Bayesian inference task.

• • …
• Example(s):
  • Clustering Exact Bayesian Inference Algorithm.
  • …
• Counter-Example(s):
  • a Approximate Bayesian Inference Algorithm.
• See: Belief Revision, Bayesian Decision.

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References

2013

• (Dechter, 2013) ⇒ Rina Dechter. (2013). “Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms.” In: Synthesis Lectures on Artificial Intelligence and Machine Learning Journal, 7(3). doi:10.2200/S00529ED1V01Y201308AIM023
  • QUOTE: In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search).

2009

• Zoubin Ghahramani. (2009). http://learning.eng.cam.ac.uk/zoubin/approx.html
  • QUOTE: For all but the simplest statistical models, exact learning and inference are computationally intractable. Approximate inference methods make it possible to learn realistic models from large data sets...

2006

• (Mott & Lester, 2006) ⇒ Bradford W. Mott, and James C. Lester. (2006). “U-director: A decision-theoretic narrative planning architecture for storytelling environments.” In: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems. doi:10.1145/1160633.1160808
  • QUOTE: Because exact inference in Bayesian networks is known to be extraordinarily inefficient (in the worst case NP-hard), U-DIRECTOR exploits recent advances in approximate Bayesian inference via stochastic sampling. The accuracy of these methods depends on the number of samples used. Moreover, stochastic sampling methods typically have an “anytime” property which is particularly attractive for real-time applications. … a performance analysis was conducted to measure the network update time using an exact Bayesian inference algorithm (Clustering [17]) and two approximate Bayesian inference algorithms (EPIS-BN [36] and Likelihood weighting [31]).

1996

• (Huang & Darwiche, 1996) ⇒ C. Huang, and A. Darwiche. (1996). “Inference in Belief Networks: a procedural guide.” In: International Journal of Approximate Reasoning, 15(3).

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