maximum entropy principle

USAGE SUMMARY

The phrase "maximum entropy principle" is correct and usable in written English.
It can be used in contexts related to statistics, information theory, or physics, where one discusses the concept of maximizing entropy to derive probability distributions. Example: "In statistical mechanics, the maximum entropy principle is used to predict the most likely distribution of particles in a system."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Among all stationary distributions those satisfying a further requirement, the Gibbsian maximum entropy principle, play a special role.

SEP

The Gibbsian maximum entropy principle then requires that SG be maximal, given the constraints that are imposed on the system.

SEP

The key idea is to implement the maximum entropy principle in estimating the failure probability function.

Probabilistic Engineering Mechanics

The maximum entropy principle is used in the parameter adjusting process.

Information Sciences

The difference was argued to originate from human neuropsychology and the maximum entropy principle.

EPJ Data Science

(Maximum entropy principle) Path configuration is determined to maximize the entropy of the paths.

Journal of Theoretical and Applied Physics

Here we derive the probability measure and amplitude from the maximum entropy principle (Principle 2).

Journal of Theoretical and Applied Physics

The probability density functions of the random variables are constructed appealing to the Maximum Entropy Principle.

Composite Structures

Here we use a nonlinear method based on the maximum entropy principle.

Journal of Computational Physics

The probability density functions of the random variables are derived with the Maximum Entropy Principle.

Composite Structures

A maximum entropy principle is developed in the general framework of the band method.

Journal of Functional Analysis

Expert writing Tips

Best practice

When using the "maximum entropy principle", ensure you clearly define the constraints you are imposing on the system, as the principle's application is highly dependent on these constraints.

Common error

Avoid assuming that the "maximum entropy principle" provides a universal solution without carefully considering the specific context and constraints of the problem. It's a tool, not a magic bullet.

Linguistic Context

The phrase "maximum entropy principle" functions as a noun phrase, typically used as a subject or object in a sentence. It represents a fundamental concept in statistics and information theory. As Ludwig AI points out, the phrase is correct and usable in English.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

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Formal & Business

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Encyclopedias

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Ludwig's WRAP-UP

The phrase "maximum entropy principle" is a well-established term, predominantly used in scientific and academic contexts. Ludwig AI confirms its correct usage in English, indicating its role as a noun phrase that introduces a core statistical and information-theoretic concept. The principle is applied to infer probability distributions when only partial information is available. Understanding its practical application and constraints is crucial to avoid misinterpretations. Alternative phrasings, such as "principle of maximum entropy", can be used for variety while maintaining semantic equivalence. Its consistent appearance in reputable sources underscores its significance in technical discourse.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

principle of maximum entropy

Reverses the order of the words, emphasizing "principle" as the main concept.

max entropy principle

Shortened version using "max" instead of "maximum".

maximum entropy method

Focuses on the application of the principle as a method.

maximum entropy approach

Highlights the strategic or methodological aspect of using the principle.

principle of maximizing entropy

Emphasizes the action of maximizing entropy.

Gibbsian maximum entropy principle

Specifies a particular formulation of the maximum entropy principle within statistical mechanics.

information-theoretic principle of maximum entropy

Highlights the link between maximum entropy and information theory.

entropic principle

Abbreviated version, suitable in contexts where "maximum entropy" is already understood.

Jaynes' principle

Refers to the principle named after Edwin Thompson Jaynes, a key figure in its development.

principle of least commitment

Describes the general approach of making minimal assumptions, which is core to maximum entropy.

FAQs

How is the "maximum entropy principle" used in practice?

The "maximum entropy principle" is used to infer probability distributions when only partial information is available. It suggests choosing the distribution that maximizes entropy, subject to the known constraints. This is applied in diverse fields like statistical mechanics, image reconstruction, and machine learning.

What does the "maximum entropy principle" tell us?

The "maximum entropy principle" suggests that, in the absence of complete information, we should choose the probability distribution that is most spread out or unbiased, as this is the distribution that makes the fewest assumptions beyond what we already know.

Is the "maximum entropy principle" the same as the "principle of least commitment"?

The "maximum entropy principle" and the "principle of least commitment" are closely related. The "maximum entropy principle" is a specific mathematical formulation of the broader idea of the "principle of least commitment", which advocates making as few assumptions as possible when solving a problem.

When is it appropriate to use the "maximum entropy principle"?

The "maximum entropy principle" is appropriate when you need to estimate a probability distribution but only have limited information in the form of constraints. If you have complete information, other methods might be more suitable.