Add to ORKGΙt is known that mere knowledge of the conditional distribution of two random variables is not sufficient to specify uniquely the marginal distributions. Some additional information is necessary. This is usually provided in some form of independence between functions of the two random variables involved. PANARETOS (1981) introduced a method of deriving the marginal distributions based on knowledge of the conditional distribution and an assumption of partial independence. An extension of this result is presented referring to truncated distributions. An interesting property of the hypergeometric distribution is revealed based on the unique decomposition of the binomial la
It is well–known that the fractional part and integer part of an exponentially distributed random va...
In a simple but mathematically coherent manner, this text examines the basis of the distribution the...
It is well known that the joint distribution of a pair of lifetime variables $X_1$ and $X_2$ which r...
Ιt is known that mere knowledge of the conditional distribution of two random variables is not suffi...
Let X, Y be two discrete random variables with finite support and X≥Y. Suppose that the conditional ...
This note is concerned with the derivation of the distribution of a random variable X in terms of th...
A theorem that gives the minimum number of values of a discrete probability distribution, which must...
We consider a number of classical and new computational problems regarding marginal distributions, a...
We recall a concept called sub-independence, which is defined in terms of the convolution of the dis...
A characterization theorem based on the proportional relation between two truncated moments is prove...
In realistic decision problems there is more often than not uncertainty in the background informatio...
We consider a number of classical and new computational problems regarding marginal distributions, a...
A distribution is conditionally specified when its model constraints are expressed conditionally. Fo...
A discrete multivariate probability distribution for dependent random variables, which contains the ...
Consider a random vector (X,Y) where X=(X1,X2, ...., Xs,) and Y=(Y1,Y2, ...., Ys) with Xi, Yi, i=1,...
It is well–known that the fractional part and integer part of an exponentially distributed random va...
In a simple but mathematically coherent manner, this text examines the basis of the distribution the...
It is well known that the joint distribution of a pair of lifetime variables $X_1$ and $X_2$ which r...
Ιt is known that mere knowledge of the conditional distribution of two random variables is not suffi...
Let X, Y be two discrete random variables with finite support and X≥Y. Suppose that the conditional ...
This note is concerned with the derivation of the distribution of a random variable X in terms of th...
A theorem that gives the minimum number of values of a discrete probability distribution, which must...
We consider a number of classical and new computational problems regarding marginal distributions, a...
We recall a concept called sub-independence, which is defined in terms of the convolution of the dis...
A characterization theorem based on the proportional relation between two truncated moments is prove...
In realistic decision problems there is more often than not uncertainty in the background informatio...
We consider a number of classical and new computational problems regarding marginal distributions, a...
A distribution is conditionally specified when its model constraints are expressed conditionally. Fo...
A discrete multivariate probability distribution for dependent random variables, which contains the ...
Consider a random vector (X,Y) where X=(X1,X2, ...., Xs,) and Y=(Y1,Y2, ...., Ys) with Xi, Yi, i=1,...
It is well–known that the fractional part and integer part of an exponentially distributed random va...
In a simple but mathematically coherent manner, this text examines the basis of the distribution the...
It is well known that the joint distribution of a pair of lifetime variables $X_1$ and $X_2$ which r...