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Likelihood function - Wikipedia

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Sorry to interrupt, but you only had a 10% chance of seeing this message This Sunday, we ask you to join the 2% of readers who give. If everyone reading this right now gave just $3, we'd hit our goal in a couple of hours. $3 is all we ask. GIVE $3 MAYBE LATER Proud hosts of Wikipedia and its sister sites The likelihood function (often simply called the likelihood) is the joint probability (or probability density) of observed data viewed as a function of the parameters of a statistical model.[1] [2] [3] In maximum likelihood estimation, the arg max (over the parameter 𝜃 ) of the likelihood function serves as a point estimate for 𝜃 , while the Fisher information (often approximated by the likelihood's Hessian matrix) indicates the estimate's precision. In contrast, in Bayesian statistics, parameter estimates are derived from the converse of the likelihood, the so-called posterior probability, which is calculated via Bayes' rule.[4] The likelihood function, parameterized by a (possibl

Likelihood function - Wikipedia Jump to content From Wikipedia, the free encyclopedia Function related to statistics and probability theory This article may be too technical for most readers to understand . Please help improve it to make it understandable to non-experts , without removing the technical details. ( August 2025 ) ( Learn how and when to remove this message ) Part of a series on Bayesian statistics Posterior = Likelihood × Prior ÷ Evidence Background Bayesian inference Bayesian probability Bayes' theorem Bernstein–von Mises theorem Coherence Cox's theorem Cromwell's rule Likelihoo

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