Estimating an extreme Bayesian network via scalings

An object-oriented Bayesian network for estimating mutation rates

Estimating the Posterior Probability of Bayesian Network

Random graphs are used to model the qualitative structure of Bayesian networks. The arc probabilities are determined by the probability that the amount of mutual information the neighbors provide to a node is greater than a given eeect size. A 2 approximation for the probability density function of mutual information in proposed. A large number of network structures in which the arc probabiliti...

Estimating Tail Decay for Stationary Sequences via Extreme Values

We study estimation of the tail decay parameter of the marginal distribution corresponding to a discrete time, real valued stationary stochastic process. Assuming that the underlying process is short-range dependent, we investigate properties of estimators of the tail decay parameter which are based on the maximal extreme value of the process observed over a sampled time interval. These estimat...

Modeling a Retweet Network via an Adaptive Bayesian Approach

Twitter (and similar microblogging services) has become a central nexus for discussion of the topics of the day. Twitter data contains rich content and structured information on users’ topics of interest and behavior patterns. Correctly analyzing and modeling Twitter data enables the prediction of the user behavior and preference in a variety of practical applications, such as tweet recommendat...

Computing optimal scalings by parametric network algorithms

A symmetric scaling of a square matrix A #0 is a matrix of the form XAX-' where X is a nonnegative, nonsingular, diagonal matrix having the same dimension of A. An asymmetric scaling of a rectangular matrix B # 0 is a matrix of the form XBY-' where X and Y"are nonnegative, nonsingular, diagonal matrices having appropriate dimensions. We consider two objectives in selecting a symmetric scaling o...