Posts tagged copula

Bayesian copula estimation: Describing correlated joint distributions

When we deal with multiple variables (e.g. \(a\) and \(b\)) we often want to describe the joint distribution \(P(a, b)\) parametrically. If we are lucky, then this joint distribution might be ‘simple’ in some way. For example, it could be that \(a\) and \(b\) are statistically independent, in which case we can break down the joint distribution into \(P(a, b) = P(a) P(b)\) and so we just need to find appropriate parametric descriptions for \(P(a)\) and \(P(b)\). Even if this is not appropriate, it may be that \(P(a, b)\) could be described well by a simple multivariate distribution, such as a multivariate normal distribution for example.

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