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The 32 pages that link to Likelihood function, each with the reason it gives.

• Bayesian inferenceRelated: It measures how well each candidate explanation predicts the observations.

• Maximum likelihood estimationBroader topic: It is the objective function that maximum likelihood estimation seeks to maximize.

• Probability density functionRelated: For continuous observations, likelihoods commonly use the model's density values.

• Sampling distributionCompared with: Likelihood varies with parameter values for fixed data; a sampling distribution varies with possible samples.

• Predictive processingRelated: It expresses how well sensory input fits a prediction.

• Bayes' theoremRelated: The bridge from evidence back to hypotheses: what carries all evidential weight in the update.

• Statistical inferenceRelated: Likelihood turns a model and observed data into evidence about unknown parameters.

• Statistical modelRelated: Likelihood measures how well candidate parameter values account for the observed data.

• Bayesian statisticsRelated: It scores parameter values by how well they predict the observed data.

• Prior probabilityRelated: The likelihood weights each hypothesis against the prior when evidence arrives.

• Bayesian probabilityRelated: It measures how well each possibility predicts the evidence used in an update.

• Bayesian decision theoryRelated: It connects possible states to the evidence used in belief updating.

• Likelihood ratioNarrower topic: Each hypothesis supplies a likelihood in the ratio’s numerator or denominator.

• Likelihood-ratio testNarrower topic: The test compares the largest likelihood values allowed by its two models.

• Bernoulli trialRelated: Observed successes and failures provide evidence for estimating a trial's unknown success probability.

• Bayes factorRelated: The Bayes factor compares data probabilities after each model averages over its parameters.

• Bayesian phylogeneticsRelated: Sequence likelihoods score how well a candidate tree and evolutionary model explain the data.

• Fisher informationNarrower topic: Fisher information quantifies the local curvature of likelihood around a parameter value.

• Model selectionRelated: Many selection criteria begin with a model’s likelihood of the observed data.

• Sufficient statisticRelated: Its factorization exposes whether the data's parameter dependence passes entirely through the statistic.

• Posterior probabilityRelated: It determines which possibilities the evidence supports more strongly.

• Naive Bayes classifierRelated: Feature likelihoods quantify how compatible each observation is with each candidate class.

• Neyman–Pearson lemmaRelated: The ratio of likelihoods under the two hypotheses determines the test statistic.

• Parameter (statistics)Related: It measures how well different parameter values account for a fixed dataset.

• Mathematical statisticsRelated: It connects observed data to parameter estimation and likelihood-based tests.

• Maximum a posteriori estimationRelated: Its peaks favor parameters that make the observed data more probable.

• Minimum description lengthRelated: For common codes, data cost is closely related to negative log likelihood.

• Thomas BayesRelated: The likelihood links a proposed cause to the observations used to assess it.

• Parametric statisticsRelated: Likelihood turns observed data into a criterion for estimating model parameters.

• Basu's theoremRelated: Likelihood factorization is a common route to proving sufficiency before applying Basu's theorem.

• Detection theoryRelated: Likelihoods rank competing explanations of the same noisy data.

• Family (statistics)Related: Likelihood compares parameter choices within a specified distribution family.