also see maths Bayes (1702-1761), as in Bayes's theorem P(H&D) = P(H).P(D|H) = P(D).P(H|D).

Styles of inference (machine learning, statistics, etc.) that rely on Bayes's theorem and the use of priors.

See supervised and unsupervised classification.

P(B|A), the probability of B given A.

A family of prior distributions is conjugate for f(x|θ) if the posterior distribution is in the family whenever the prior is in the family; e.g., a beta prior distribution for the parameter of a binom [..]

An estimator is consistent if it converges to the correct estimate (assuming that the model class includes the true model) as more and more data are made available.

Machine learning + some aspects of data bases, with the emphasis on (very) large data sets and efficient and robust (and sometimes ad hoc) methods. (If you know of a better, short definition, tell me. [..]

Sample Space: Set of values from which data are drawn, e.g., Throw={head, tail}.

Theta-hat, a value of a parameter, theta, inferred from (i.e., fitted to) data.

A function (mapping) from the data-space to the space of parameter values.