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What is another word for Maximum Likelihood Estimates?

Pronunciation: [mˈaksɪməm lˈa͡ɪklihˌʊd ˈɛstɪməts] (IPA)

Maximum likelihood estimates are statistical estimates derived from the principle of maximum likelihood, a widely used method in statistics. The purpose of this approach is to find the parameter values that maximize the likelihood function, thereby making the observed data most probable. However, it is worth noting that synonyms for this term are available, such as "MLE" or "maximum likelihood estimation". These synonyms are commonly used in academic literature and statistical discussions, serving as convenient shorthand for the concept. Regardless of the term used, understanding maximum likelihood estimates is essential for accurately estimating statistical parameters and making informed inferences.
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What are the opposite words for Maximum Likelihood Estimates?

The antonyms for the term "maximum likelihood estimates" can be referred to as minimum likelihood estimates. While maximum likelihood estimates aim to determine the most likely parameters of a statistical model, minimum likelihood estimates, on the other hand, seek to identify the least probable parameters. They are the inverse of each other and are often used to examine the bounds of the parameter space. While maximum likelihood estimates tend to be more common in statistical modeling, minimum likelihood estimates have their uses, particularly in cases where it is more important to bound the parameter space than to find the most likely parameter value.

What are the antonyms for Maximum likelihood estimates?

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