# distribution

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Related to T-distribution: T-test

## distribution

n. the act of dividing up the assets of an estate or trust, or paying out profits or assets of a corporation or business according to the ownership percentages. (See: distribute)

## distribution

1 the apportioning of the estate of a deceased intestate among the persons entitled to share in it.
2 after a bankruptcy order has been made, the trustee, having gathered in the bankrupt's estate, must distribute the assets available for distribution in accordance with the prescribed order of payment. All debts proved in the bankruptcy in the same category of priority rank PARI PASSU. See also CORPORATION TAX.

DISTRIBUTION. By this term is understood the division of an intestate's estate according to law.
2. The English statute of 22 and 23 Car. II. c. 10, which was itself probably borrowed from the 118th Novel of Justinian, is the foundation of, perhaps, most acts of distribution in the several states. Vide 2 Kent, Com. 342, note; 8 Com. Dig. 522; 11 Vin. Ab. 189, 202; Com. Dig. Administration, H.

References in periodicals archive ?
An important parameter in the t-distribution is the degrees of freedom.
Table 1: Two-Tailed f-values for Selected Confidence Levels (%) and 30 Degrees of Freedom (df) Confidance [alpha] df Two-Tailed Interval % t-value 99% 0.01 30 2.75 95% 0.050 30 2.042 90% 0.100 30 1.697 80% 0.200 30 1.310 75% 0.250 30 1.173 67% 0.333 30 0.983 Note: The Excel formula = T.INV.2T(probability,deg_freedom) returns the two-tailed inverse of the t-distribution. That is, it provides the t-value (of the t-distribution) based on a specified two-tailed probability ([alpha] = 1--confidence level %) and the number of degrees of freedom (df) associated with the t-distribution.
Similar to traditional RBM, the relational expression of joint distribution and energy function of the RBM with T-distribution function is:
In the t-distribution, the inverse of the degree of freedom, 1/m, acts as the expansion parameter to quantify the approach to the Gaussian distribution in large m limit.
Amongst the subclasses, Student's t-distribution has the highest log-likelihood and the smallest AIC for AGL and IMP, while ANG, BIL, and GFI are best modeled by the NIG distribution according to the LL and AIC criteria.
Though the final project uses the t-distribution, students cannot fully appreciate the t-distribution and the particular conditions governing use of t or without first understanding the x and distributions.
One additional issue emerges when computing the probability of coverage of the confidence intervals for [[sigma].sup.2.sub.c]: the population value of [[sigma].sup.2.sub.c] under the multivariate t-distribution assumption is different from the one under the multivariate normality assumption.
Since the effective degree of freedom is 6, from t-distribution table at 95% CL the coverage factor K = 2.45
The coverage factor k for 9 degrees of freedom based on Student's t-distribution for "about 95%" confidence interval is 2.32.
The complete model specification implies that log(cost) follows a t-distribution with v degrees of freedom (Verdinelli and Wasserman 1991; Gelman et al.
Assuming that the {[r.sub.i]} are independent and identically distributed normal random variables, t has a central t-distribution with n - 1 degrees of freedom if [Rho] [is not equal to] [[Rho].sub.goal], and a noncentral t-distribution if [Rho] [is not equal to] [[Rho].sub.goal] (Lehmann, 1986).
For the monotonically increasing calibration curve, the variable t has a noncentral t-distribution, as described by Graybill (13) with noncentrality parameter:

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