Index: The Book of Statistical ProofsModel Selection ▷ Bayesian model selection ▷ Posterior model probability ▷ Definition

Definition: Let $m_1, \ldots, m_M$ be $M$ statistical models with model evidences $p(y \vert m_1), \ldots, p(y \vert m_M)$ and prior probabilities $p(m_1), \ldots, p(m_M)$. Then, the conditional probability of model $m_i$, given the data $y$, is called the posterior probability of model $m_i$:

$\label{eq:PMP} \mathrm{PP}(m_i) = p(m_i|y) \; .$

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Metadata: ID: D87 | shortcut: pmp | author: JoramSoch | date: 2020-07-28, 03:30.