Deterministic Approximate Bayesian Inference Algorithm

A Deterministic Approximate Bayesian Inference Algorithm is an approximate Bayesian inference algorithm that Optimizes an Analytical Approximation Function to the Posterior Probability Function an can be implemented by a deterministic approximate Bayesian inference system (to solve a deterministic approximate Bayesian inference task).

• Context:
  • It can assume that the function:
    • has a parametric Gaussian form
    • factorizes
• Example(s):
  • Variational Inference Algorithm.
  • Expectation Propagation Algorithm.
  • …
• Counter-Example(s):
  • Deterministic Exact Bayesian Inference Algorithm.
  • Stochastic Approximate Bayesian Inference Algorithm.
• See: Approximate Bayesian Inference.

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References

2006

• (Bishop, 2006) ⇒ Christopher M. Bishop. (2006). “Pattern Recognition and Machine Learning." Springer, Information Science and Statistics. ISBN:0387310738
  • QUOTE: In this chapter, we introduce a range of deterministic approximation schemes, some of which scale well to large applications. These are based on analytical approximations to the posterior distribution, for example by assuming that it factorizes in a particular way or that it has a specific parametric form such as a Gaussian. As such, they can never generate exact results, and so their strengths and weaknesses are complementary to those of sampling methods.

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