Sparse and Crowded Cells and Dirichlet Distributions
نویسندگان
چکیده
منابع مشابه
Sparse Sequential Dirichlet Coding
This short paper describes a simple coding technique, Sparse Sequential Dirichlet Coding, for multi-alphabet memoryless sources. It is appropriate in situations where only a small, unknown subset of the possible alphabet symbols can be expected to occur in any particular data sequence. We provide a competitive analysis which shows that the performance of Sparse Sequential Dirichlet Coding will ...
متن کاملOn Dirichlet Multinomial Distributions
Dedicated to Professor Y. S. Chow on the Occasion of his 80th Birthday By Robert W. Keener and Wei Biao Wu Abstract Let Y have a symmetric Dirichlet multinomial distributions in R, and let Sm = h(Y1)+· · ·+h(Ym). We derive a central limit theorem for Sm as the sample size n and the number of cells m tend to infinity at the same rate. The rate of convergence is shown to be of order m. The approa...
متن کاملDistributions and Analytic Continuation of Dirichlet Series
Dirichlet series and Fourier series can both be used to encode sequences of complex numbers an , n ∈ N. Dirichlet series do so in a manner adapted to the multiplicative structure of N, whereas Fourier series reflect the additive structure of N. Formally at least, the Mellin transform relates these two ways of representing sequences. In this paper, we make sense of the Mellin transform of period...
متن کاملSparse and Evasive Pseudorandom Distributions
Pseudorandom distributions on n -bit strings are ones which cannot be efficiently distinguished from the uniform distribution on strings of the same length. Namely, the expected behavior of any polynomial-time algorithm on a pseudorandom input is (almost) the same as on a random (Le. unifo~y chosen) input. Clearly, the uniform distribution is a pseudorandom one. But do such trivial cases exhaus...
متن کاملAsymptotics for Constrained Dirichlet Distributions
We derive the asymptotic approximation for the posterior distribution when the data are multinomial and the prior is Dirichlet conditioned on satisfying a finite set of linear equality and inequality constraints so the posterior is also Dirichlet conditioned on satisfying these same constraints. When only equality constraints are imposed, the asymptotic approximation is normal. Otherwise it is ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1974
ISSN: 0090-5364
DOI: 10.1214/aos/1176342818