maximum likelihood estimation: meaning, definition, pronunciation and examples

C2
UK/ˌmæk.sɪ.məm ˈlaɪ.kli.hʊd ˌes.tɪˈmeɪ.ʃən/US/ˌmæk.sə.məm ˈlaɪ.kli.hʊd ˌes.təˈmeɪ.ʃən/

Formal, Technical, Academic

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Quick answer

What does “maximum likelihood estimation” mean?

A method in statistics for estimating the parameters of a statistical model, selecting the parameter values that maximize the likelihood function (i.

Audio

Pronunciation

Definition

Meaning and Definition

A method in statistics for estimating the parameters of a statistical model, selecting the parameter values that maximize the likelihood function (i.e., the probability of observing the given data).

A fundamental inference procedure where parameter estimates are chosen to make the observed sample data most probable under an assumed probability model. It is a cornerstone of frequentist statistics, with widespread applications in machine learning, econometrics, and scientific modeling.

Dialectal Variation

British vs American Usage

Differences

No significant lexical or syntactic differences. Spelling follows regional conventions (e.g., BrE 'likelihood', AmE 'likelihood'—same spelling). Pronunciation may differ slightly.

Connotations

Identical technical connotations in both varieties.

Frequency

Equally frequent in academic and technical contexts in both regions.

Grammar

How to Use “maximum likelihood estimation” in a Sentence

[Subject] performs maximum likelihood estimation on [data] to estimate [parameters].Maximum likelihood estimation of [parameter] yields [result].We employed maximum likelihood estimation.The estimates were obtained by maximum likelihood estimation.

Vocabulary

Collocations

strong
perform maximum likelihood estimationuse maximum likelihood estimationMLE (abbreviation)maximum likelihood estimatorasymptotic properties of maximum likelihood estimation
medium
derive via maximum likelihood estimationbased on maximum likelihood estimationprinciples of maximum likelihood estimationcomputationally intensive maximum likelihood estimation
weak
robust maximum likelihood estimationdiscuss maximum likelihood estimationapplication of maximum likelihood estimationchapter on maximum likelihood estimation

Examples

Examples of “maximum likelihood estimation” in a Sentence

verb

British English

  • To fit the model, we need to maximise the likelihood function.
  • The parameters were estimated by maximising the likelihood.

American English

  • To fit the model, we need to maximize the likelihood function.
  • We estimated the parameters using likelihood maximization.

adverb

British English

  • The parameters were estimated maximum-likelihood.
  • This is not typically used adverbially.

American English

  • The parameters were estimated via maximum likelihood.
  • This is not typically used adverbially.

adjective

British English

  • The maximum-likelihood approach is standard.
  • She presented the maximum-likelihood results.

American English

  • The maximum likelihood approach is standard.
  • She presented the maximum likelihood results.

Usage

Meaning in Context

Business

Rare, except in specialized quantitative finance or market research analytics.

Academic

Ubiquitous in statistics, econometrics, biostatistics, psychology, and machine learning papers and textbooks.

Everyday

Virtually never used.

Technical

Core terminology in data science, engineering, and any field involving statistical modeling and inference.

Vocabulary

Synonyms of “maximum likelihood estimation”

Strong

likelihood maximization

Neutral

MLE

Weak

parametric estimation (broader category)point estimation method (broader category)

Vocabulary

Antonyms of “maximum likelihood estimation”

method of momentsBayesian estimationleast squares estimation (in specific contexts)minimum distance estimation

Watch out

Common Mistakes When Using “maximum likelihood estimation”

  • Saying 'maximum likelihood estimator' when referring to the method (estimation) rather than the resulting formula (estimator).
  • Confusing it with 'maximum a posteriori estimation' (MAP), which incorporates prior beliefs.
  • Using it as a verb: Incorrect: 'We will maximum likelihood estimate the parameter.' Correct: 'We will estimate the parameter using maximum likelihood.'

FAQ

Frequently Asked Questions

The main idea is to choose the parameter values for a model that make the observed sample data you have collected the most probable or 'likely' to have occurred.

No. MLE is a frequentist method that uses only the observed data. Bayesian estimation incorporates prior beliefs about the parameters (a prior distribution) and updates them with the data to form a posterior distribution.

It refers to how the MLE behaves as the sample size becomes very large. Key asymptotic properties include consistency (it converges to the true parameter value), normality (its distribution becomes normal), and efficiency (it achieves the lowest possible variance among consistent estimators).

MLE can be problematic with very small sample sizes, when the likelihood function is flat or has multiple maxima, when computational complexity is too high, or when a full probabilistic model for the data cannot be or is not specified.

A method in statistics for estimating the parameters of a statistical model, selecting the parameter values that maximize the likelihood function (i.

Maximum likelihood estimation is usually formal, technical, academic in register.

Maximum likelihood estimation: in British English it is pronounced /ˌmæk.sɪ.məm ˈlaɪ.kli.hʊd ˌes.tɪˈmeɪ.ʃən/, and in American English it is pronounced /ˌmæk.sə.məm ˈlaɪ.kli.hʊd ˌes.təˈmeɪ.ʃən/. Tap the audio buttons above to hear it.

Phrases

Idioms & Phrases

  • The gold standard of estimation (informal, context-dependent)

Learning

Memory Aids

Mnemonic

Think of a detective (the estimator) looking for the most LIKELY suspect (parameter value) based on the MAXIMUM amount of evidence (data).

Conceptual Metaphor

ESTIMATION IS A SEARCH (for the best parameter value). LIKELIHOOD IS A HEIGHT (to be maximized on a surface).

Practice

Quiz

Fill in the gap
In our statistical analysis, we employed to find the parameter values that made the observed data most probable.
Multiple Choice

Maximum likelihood estimation is primarily associated with which statistical paradigm?

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