central moment वाक्य
उदाहरण वाक्य
मोबाइल
- The mean, variance and third central moment of this distribution have been determined
- They are being measured as the n-th radical of the n-th central moment.
- The normalised third central moment is called the skewness, often.
- To illustrate this, examine the following 4th-order central moment case:
- The fourth central moment of a normal distribution is.
- These are central moments in each of our histories.
- The notation ? 3 means the third central moment.
- Those agreements are still in place, even though the story's central moments are in serious question.
- He tried to portray Wednesday's Senate action as a central moment in a far larger debate.
- The sign of its third central moment is determined by the difference ( ? 2-? 1 ).
- So for example an unbiased estimate of the population variance ( the second central moment ) is given by
- This contrasts with the situation for central moments, whose computation uses up a degree of freedom by using the sample mean.
- Swimmy forcefully states, " I will be the eye " of the fish, the fable's " central moment, " in Lionni's view.
- To express the cumulants for as functions of the standardized central moments, also set 2 = 1 } } in the polynomials:
- It also became a central moment in the history of Micronesian studies and became the start of modern ethnography in that area.
- "' Color moments "'are measures that characterise color distribution in an image in the same way that central moments uniquely describe a probability distribution.
- However, while " moment invariants " are invariants that are formed from moments, the only moments that are invariants themselves are the central moments.
- To express the cumulants for as functions of the central moments, drop from these polynomials all terms in which ?'1 appears as a factor:
- The central moment for many students at the conference is submitting one's own writing for critique by 15 or so fellow students and the instructor.
- The fourth central moment is a measure of the heaviness of the tail of the distribution, compared to the normal distribution of the same variance.
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