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Everything about Likelihood function
Function related to statistics and probability theory
A likelihood function gives the relative merit of various statistical models for describing a data set. Often the models being compared are parameterized by a parameter, with the parameter often written as θ, or they are parameterized by multiple parameters given as the components of a vector. For a probability function Pr[x | θ] that gives the probability of data x for a given model-specifying parameter θ, the likelihood is any function of θ equal to cPr[x | θ] for some positive value c. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Likelihood functionA 5-minute orientation
- Skim a recent paper: A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer InterferometryInfluential work since 2022
- Pick up a classic book: Bayesian methods for finite population samplingThe most-reprinted book on the subject
- Read the landmark paper: An extended quasi-likelihood functionCited 280 times
- Go sideways: Relative likelihoodA closely related topic
- Go sideways: Point estimationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
An extended quasi-likelihood function
Simple algorithm for a maximum-likelihood SAD function
Interval Estimation from the Likelihood Function
Asymptotic relations between the likelihood estimating function and the maximum likelihood estimator
Maximum Likelihood Estimation of a Unimodal Density Function
An approximation to the modified profile likelihood function
THE LIKELIHOOD EQUATION, CONSISTENCY AND THE MAXIMA OF THE LIKELIHOOD FUNCTION
Copula cosmology: Constructing a likelihood function
🆕 Influential since 2022
A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry
A deep hashing method of likelihood function adaptive mapping
Bayesian model updating utilizing scaled likelihood ratio and BCT-PCA with frequency response function
Surface wave dispersion inversion using an energy likelihood function
Nonparametric maximum likelihood estimation of the distribution function using ranked-set sampling
📚 Books worth reading
Bayesian methods for finite population samplingMalay Ghosh · 1997
Parsimony, Phylogeny, and GenomicsVictor Albert · 2005
Statistical evidenceRichard M. Royall · 1997- 📕Weight-of-evidence for forensic DNA profilesD. J. Balding · 2005
- 📕Approximate confidence regions for the parameters of three software reliability modelsG. Moek · 1987
- 📕Estimation and classification by sigmoids based on mutual informationYoram Baram · 1994
Statistical Inference Based on the likelihood (Monographs on Statistics and Applied Probability)Adelchi Azzalini · 1996
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Relative likelihood In statistics, when selecting a statistical model for given data, the relative likelihood compares the relative plausibilities of different candidate models…
- Point estimation In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate, since it identifies a point rather…
- Maximum likelihood estimation In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed…
- Sufficient statistic In statistics, sufficiency is a property of a statistic computed on a sample dataset in relation to a parametric model of the dataset. A sufficient statistic…
- Parametric statistics Parametric statistics is a branch of statistics that is concerned with the analysis of and inference from data assuming that the underlying distribution…
- Informant function In statistics, the informant or score is the gradient of the log-likelihood function with respect to the parameter vector. Evaluated at a particular value…
- Expectation–maximization algorithm In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates…
- Fisher information In mathematical statistics, the Fisher information is a way of measuring the amount of information that an observable random variable X carries about an…