A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability...

distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is...

distribution resulting from applying Bayes theorem to a binomial likelihood function and a prior probability, the interpretation of the addition of both...

measure of goodness-of-fit is the likelihood function L, or its logarithm, the log-likelihood ℓ. The likelihood function L is analogous to the ε 2 {\displaystyle...

In Bayesian probability theory, if, given a likelihood function p ( x ∣ θ ) {\displaystyle p(x\mid \theta )} , the posterior distribution p ( θ ∣ x )...

statistics, the score (or informant) is the gradient of the log-likelihood function with respect to the parameter vector. Evaluated at a particular point...

A marginal likelihood is a likelihood function that has been integrated over the parameter space. In Bayesian statistics, it represents the probability...

the function above as the definition. Thus, the likelihood ratio is small if the alternative model is better than the null model. The likelihood-ratio...

known, the log likelihood of an observed vector x {\displaystyle {\boldsymbol {x}}} is simply the log of the probability density function: ln ⁡ L ( x )...

is contained in the likelihood function. A likelihood function arises from a probability density function considered as a function of its distributional...