The Dirichlet Markov Ensemble
نویسنده
چکیده
We equip the polytope of n × n Markov matrices with the normalized trace of the Lebesgue measure of Rn 2 . This probability space provides random Markov matrices, with i.i.d. rows following the Dirichlet distribution of mean (1/n, . . . , 1/n). We show that ifM is such a randommatrix, then the empirical distribution built from the singular values of √ nM tends as n → ∞ to a Wigner quarter–circle distribution. Some computer simulations reveal striking asymptotic spectral properties of such random matrices, still waiting for a rigorous mathematical analysis. In particular, we believe that with probability one, the empirical distribution of the complex spectrum of √ nM tends as n → ∞ to the uniform distribution on the unit disc of the complex plane, and that moreover, the spectral gap of M is of order 1− 1/√n when n is large. AMS 2000 Mathematical Subject Classification: 15A52; 15A51; 15A42; 60F15; 62H99.
منابع مشابه
Detecting Abnormal Events via Hierarchical Dirichlet Processes
Detecting abnormal event from video sequences is an important problem in computer vision and pattern recognition and a large number of algorithms have been devised to tackle this problem. Previous state-based approaches all suffer from the problem of deciding the appropriate number of states and it is often difficult to do so except using a trial-and-error approach, which may be infeasible in r...
متن کاملLearning Bayesian Network Structure using Markov Blanket in K2 Algorithm
A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
متن کاملA Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population co...
متن کاملHyper Markov Non-Parametric Processes for Mixture Modeling and Model Selection
Markov distributions describe multivariate data with conditional independence structures. Dawid and Lauritzen (1993) extended this idea to hyper Markov laws for prior distributions. A hyper Markov law is a distribution over Markov distributions whose marginals satisfy the same conditional independence constraints. These laws have been used for Gaussian mixtures (Escobar, 1994; Escobar and West,...
متن کاملApproximation of Arbitrary Dirichlet Processes by Markov Chains 1);2)
We prove that any Hunt process on a Hausdorr topological space associated with a Dirichlet form can be approximated by a Markov chain in a canonical way. This also gives a new and \more explicit" proof for the existence of Hunt processes associated with strictly quasi-regular Dirichlet forms on general state spaces.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- J. Multivariate Analysis
دوره 101 شماره
صفحات -
تاریخ انتشار 2010