Specification and classes
A distribution is often specified by a pdf or a cdf involving certain parameters. Or it may be specified by a stochastic process generating some values: ie in terms of other other distributions.
Sometimes, the density specified need not even be proper (sum/ integrate to 1) to be useful: Eg: In applying the conditional probability inversion trick.
Notation
If the pdf of \(X\) is a member (identified by the parameter \(p_1\)) of the function family \(\set{f(p)}\), we write \(X \distr f(p_1)\).
Parameter types
Location parameters specify important points in the distribution: Eg: \(\mean\) in \(N(\mean, \stddev^{2})\) distribution.
Scale parameters specify how spread-out the distribution is. A parameter \(s\) is a scale parameter if, having set the location parameter to 0, \(CDF(x; ks) = CDF(x/k; s)\) Eg: \(h\) in \(C(x; \mean, h)\) distribution, and \(\stddev\) in \(N(\mean, \stddev^{2})\).
All other parameters are called shape parameters.
Specify continuous distribution over bounded support
Take Indicator fn \(I_{(a,b)}\): See algebra ref. So, if U(a,b): \(f(x) = (b-a)^{-1}I_{(a,b)}(x)\). Not differentiable in boundaries.
Inference, Sampling from distribution
See randomized algorithms ref.