cauchy distribution वाक्य
उदाहरण वाक्य
मोबाइल
- The characteristic function representation for the wrapped Cauchy distribution in the left side of the integral is:
- A " t " statistic generated from data set drawn from a Cauchy distribution is bimodal.
- Important special cases of stable distributions are the normal distribution, the Cauchy distribution and the L�vy distribution.
- Since the Cauchy distribution is a stable distribution, the log-Cauchy distribution is a logstable distribution.
- Since the Cauchy distribution is a stable distribution, the log-Cauchy distribution is a logstable distribution.
- An example where the law of large numbers does " not " apply is the Cauchy distribution.
- But in the case of the Cauchy distribution, both the positive and negative terms of ( 2 ) are infinite.
- The expected value does not exist for random variables having some large " tails ", such as the Cauchy distribution.
- There exist elliptical distributions that have undefined mean, such as the Cauchy distribution ( even in the univariate case ).
- Neither the normal distribution nor the ratio distribution of two normal distributions ( the Cauchy distribution ) obey Benford's law.
- Other examples of distributions that are not exponential families are the F-distribution, Cauchy distribution, hypergeometric distribution and logistic distribution.
- Other examples of unimodal distributions include Cauchy distribution, Student's t-distribution, chi-squared distribution and exponential distribution.
- Some authors define \ mu and \ sigma as the location and scale parameters, respectively, of the log-Cauchy distribution.
- It has the standard resonance form of the Lorentz, or Cauchy distribution, but involves relativistic variables = ? here = 2.
- This is the characteristic function of the standard Cauchy distribution : thus, the sample mean has the same distribution as the population itself.
- For \ mu = 0 and \ sigma = 1, corresponding to a standard Cauchy distribution, the probability density function reduces to:
- Generally, a "'Cauchy distribution "'is any probability distribution belonging to the same location-scale family as this one.
- If a continuous distribution does not have an expected value, as is the case for the Cauchy distribution, it does not have a variance either.
- Like all stable distributions, the location-scale family to which the Cauchy distribution belongs is closed under linear transformations with linear fractional transformations with real coefficients.
- However, because of the fat tails of the Cauchy distribution, the efficiency of the estimator decreases if more than 24 % of the sample is used.
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