"Likelihood Functions" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
Descriptor ID 
D016013

MeSH Number(s) 
E05.318.740.500.475 E05.318.740.600.400 E05.599.835.500 N05.715.360.750.530.450 N05.715.360.750.625.450 N06.850.520.830.500.475 N06.850.520.830.600.400

Concept/Terms 
Likelihood Functions Likelihood Functions
 Function, Likelihood
 Functions, Likelihood
 Likelihood Function
Maximum Likelihood Estimates Maximum Likelihood Estimates
 Estimate, Maximum Likelihood
 Estimates, Maximum Likelihood
 Maximum Likelihood Estimate

Below are MeSH descriptors whose meaning is more general than "Likelihood Functions".
Below are MeSH descriptors whose meaning is more specific than "Likelihood Functions".
This graph shows the total number of publications written about "Likelihood Functions" by people in this website by year, and whether "Likelihood Functions" was a major or minor topic of these publications.
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Year  Major Topic  Minor Topic  Total 

1995  0  2  2 
1997  0  3  3 
1998  0  3  3 
2000  0  3  3 
2001  0  1  1 
2002  0  4  4 
2003  0  2  2 
2004  0  3  3 
2005  0  2  2 
2006  0  5  5 
2007  0  3  3 
2008  0  2  2 
2009  0  3  3 
2010  0  2  2 
2012  0  2  2 
2013  0  6  6 
2014  0  2  2 
2015  1  0  1 
2016  0  4  4 
2017  0  1  1 
2018  1  0  1 
2019  1  3  4 
2022  0  1  1 
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Below are the most recent publications written about "Likelihood Functions" by people in Profiles.

Lucas BA, Zhang K, Loerch S, Grigorieff N. In situ single particle classification reveals distinct 60S maturation intermediates in cells. Elife. 2022 08 25; 11.

Subramaniyam S, DeJesus MA, Zaveri A, Smith CM, Baker RE, Ehrt S, Schnappinger D, Sassetti CM, Ioerger TR. Statistical analysis of variability in TnSeq data across conditions using zeroinflated negative binomial regression. BMC Bioinformatics. 2019 Nov 21; 20(1):603.

Bakbergenuly I, Hoaglin DC, Kulinskaya E. Estimation in metaanalyses of mean difference and standardized mean difference. Stat Med. 2020 01 30; 39(2):171191.

Pituch KA, Joshi M, Cain ME, Whittaker TA, Chang W, Park R, McDougall GJ. The Performance of Multivariate Methods for TwoGroup Comparisons with Small Samples and Incomplete Data. Multivariate Behav Res. 2020 SepOct; 55(5):704721.

Bakbergenuly I, Hoaglin DC, Kulinskaya E. Pitfalls of using the risk ratio in metaanalysis. Res Synth Methods. 2019 Sep; 10(3):398419.

Fischer DS, Fiedler AK, Kernfeld EM, Genga RMJ, BastidasPonce A, Bakhti M, Lickert H, Hasenauer J, Maehr R, Theis FJ. Inferring population dynamics from singlecell RNAsequencing time series data. Nat Biotechnol. 2019 04; 37(4):461468.

Qian J, Ray E, Brecha RL, Reilly MP, Foulkes AS. A likelihoodbased approach to transcriptome association analysis. Stat Med. 2019 04 15; 38(8):13571373.

Ronzitti S, Kraus SW, Hoff RA, Clerici M, Potenza MN. Problemgambling severity, suicidality and DSMIV Axis II personality disorders. Addict Behav. 2018 07; 82:142150.

Ghammraoui B, Glick SJ. Investigating the feasibility of classifying breast microcalcifications using photoncounting spectral mammography: A simulation study. Med Phys. 2017 Jun; 44(6):23042311.

Jain NB, Luz J, Higgins LD, Dong Y, Warner JJ, Matzkin E, Katz JN. The Diagnostic Accuracy of Special Tests for Rotator Cuff Tear: The ROW Cohort Study. Am J Phys Med Rehabil. 2017 Mar; 96(3):176183.