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CHARACTERIZATIONS OF MULTINOMIAL DISTRIBUTIONS BASED ON CONDITIONAL DISTRIBUTIONS
, 1994
"... ABSTRACT. Several characterizations of the joint multinomial distribution of two discrete random vectors are derived assuming conditional multinomial distributions. ..."
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ABSTRACT. Several characterizations of the joint multinomial distribution of two discrete random vectors are derived assuming conditional multinomial distributions.
Bayesian inference, entropy, and the multinomial distribution
, 2007
"... Instead of maximumlikelihood or MAP, Bayesian inference encourages the use of predictive densities and evidence scores. This is illustrated in the context of the multinomial distribution, where predictive estimates are often used but rarely described as Bayesian. By using an entropy approximation t ..."
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Cited by 21 (1 self)
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Instead of maximumlikelihood or MAP, Bayesian inference encourages the use of predictive densities and evidence scores. This is illustrated in the context of the multinomial distribution, where predictive estimates are often used but rarely described as Bayesian. By using an entropy approximation
The Multinomial Distribution on Rooted Labeled Forests
, 1997
"... For a probability distribution (p s ; s 2 S) on a finite set S, call a random forest F of rooted trees labeled by S (with edges directed away from the roots) a pforest if given F has m edges the vector of outdegrees of vertices of F has a multinomial distribution with parameters m and (p s ; s 2 ..."
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Cited by 8 (8 self)
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For a probability distribution (p s ; s 2 S) on a finite set S, call a random forest F of rooted trees labeled by S (with edges directed away from the roots) a pforest if given F has m edges the vector of outdegrees of vertices of F has a multinomial distribution with parameters m and (p
Detecting Deviation in Multinomially Distributed Data
"... Multinomial models are used in describing the distribution of categorial or discrete variables. In practice we are often interested whether a given sample deviates significantly from a certain multinomial distribution. To determine this, often Pearson’s χ2 or likelihood ratio tests are used. Besid ..."
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Multinomial models are used in describing the distribution of categorial or discrete variables. In practice we are often interested whether a given sample deviates significantly from a certain multinomial distribution. To determine this, often Pearson’s χ2 or likelihood ratio tests are used
Determining the Parameters of a Multinomial Distribution: The Fiducial Approach
"... Abstract: A procedure is derived for determining the values of the parameters of a multinomial distribution by means of the fiducial approach proposed by Fisher. By decomposing the involved probability (mass or density) functions, it is shown that the resulting fiducial distributions belong to the D ..."
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Abstract: A procedure is derived for determining the values of the parameters of a multinomial distribution by means of the fiducial approach proposed by Fisher. By decomposing the involved probability (mass or density) functions, it is shown that the resulting fiducial distributions belong
On a limiting quasimultinomial distribution
, 2005
"... A random clustering distribution is useful for modeling count data. The present article derives a new distribution of this type from the Lagrangian Poisson distribution, based on the result that any infinitely divisible distribution over nonnegative integers produces a random clustering distribution ..."
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A random clustering distribution is useful for modeling count data. The present article derives a new distribution of this type from the Lagrangian Poisson distribution, based on the result that any infinitely divisible distribution over nonnegative integers produces a random clustering
Maximization of the information divergence from multinomial distributions
"... SUMMARY Explicit solution of the problem of maximization of information divergence from the family of multinomial distributions is presented. General problem of maximization of information divergence from an exponential family has emerged in probabilistic models for evolution and learning in neural ..."
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SUMMARY Explicit solution of the problem of maximization of information divergence from the family of multinomial distributions is presented. General problem of maximization of information divergence from an exponential family has emerged in probabilistic models for evolution and learning
Discretized multinomial distributions and Nash equilibria in anonymous games
 In Proc. 49th Symp. Foundations of Computer Science (FOCS
, 2008
"... We show that there is a polynomialtime approximation scheme for computing Nash equilibria in anonymous games with any fixed number of strategies (a very broad and important class of games), extending the twostrategy result of [16]. The approximation guarantee follows from a probabilistic result of ..."
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Cited by 20 (4 self)
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of more general interest: The distribution of the sum of n independent unit vectors with values ranging over {e1,...,ek}, where ei is the unit vector along dimension i of the kdimensional Euclidean space, can be approximated by the distribution of the sum of another set of independent unit vectors whose
Rao's Distance For Negative Multinomial Distributions
 SANKHYA. THE INDIAN JOURNAL OF STATISTICS, SERIES A
, 1985
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RESEARCH ARTICLE Overdispersion at the Binomial and Multinomial Distribution
, 2011
"... Overdispersion is a widely discussed phenomenon in case of binomial and Poisson distributed data. We analysed multinomial data with varying multinomial parameters as a part of attribute gauge study. In this situation overdispersion is expected to occur. However in case of nonrepeated observations ..."
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Overdispersion is a widely discussed phenomenon in case of binomial and Poisson distributed data. We analysed multinomial data with varying multinomial parameters as a part of attribute gauge study. In this situation overdispersion is expected to occur. However in case of nonrepeated observations
Results 1  10
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49,520