Approximate inference
In today's world, Approximate inference has become a topic of great importance and interest to a wide variety of people. Whether we are talking about Approximate inference as a historical figure, an abstract concept or a current topic, its relevance and impact transcend barriers and borders, impacting people of different ages, cultures and professions. In this article, we will seek to explore and analyze different aspects related to Approximate inference, with the aim of providing a comprehensive and enriching vision of this topic that is so significant today.
Approximate inference methods make it possible to learn realistic models from big data by trading off computation time for accuracy, when exact learning and inference are computationally intractable.
Major methods classes
- Laplace's approximation
- Variational Bayesian methods
- Markov chain Monte Carlo
- Expectation propagation
- Markov random fields
- Bayesian networks
- Loopy and generalized belief propagation
See also
References
- ^ "Approximate Inference and Constrained Optimization". Uncertainty in Artificial Intelligence: 313–320. 2003.
- ^ "Approximate Inference". Retrieved 2013-07-15.
External links
- Tom Minka, Microsoft Research (Nov 2, 2009). "Machine Learning Summer School (MLSS), Cambridge 2009, Approximate Inference" (video lecture).