Bayesian Inference

Bayesian inference
Our next example, Bayesian inference, deals with evidential uncertainty. Thus we interpret the two inputs as being evidence for the output.

Bayesian inference
Bayesian probability Â- Prior Â- Posterior Â- Credible interval Â- Bayes factor Â- Bayesian estimator Â- Maximum posterior estimator
Frequentist inference ...

Bayesian inference in dynamic models -- an overview
Mixture of Experts A finite mixture model for random variable y where all the components and the distribution over components are conditional on measurement x.

Bayesian networks[95] have been applied to a large number of problems, such as: uncertain reasoning (using the Bayesian inference algorithm),[96] learning (using the expectation-maximization algorithm),[97] and planning (using decision networks).

In the last few years, there has been a real movement of the discipline in three different directions: Neural networks, statistics, generative models, Bayesian inference There is a sense in which these fields are coalescing.

FAQs of the newsgroup comp.ai.neural-nets
An online textbook on Information Theory, Bayesian inference, and neural networks
University of Texas Neural Network Research Group's archive of papers, demos, and software ...

A formal framework for making logical decisions in problem areas containing risk, uncertainty and probabilities, typically employing Bayesian inference methods.
Decision tree ...

" AAAI "Classic Paper" Award in 2000 for revolutionizing uncertain reasoning through the introduction of efficient Bayesian inference methods.

novelty reward might be more complicated to compute than, say, a food reward. Specifically, a novelty detection system would probably be based on prediction errors (e.g., the difference between the prior and posterior, in terms of Bayesian inference).

See also: Inference, Machine learning, Knowledge, Demon, Neural network