Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering { uncertainty and complexity { and in particular they ar
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in ma
Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability) (Hardcover) by P. McCullagh P. McCullagh (Author) › Visit Amazon's P. McCullagh Page Find all the books, read about the author, and more. See search results
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamenta
I take the central task of theoretical computing science (TCS) to be the construction of mathematical models of computational phenomena. Such models provide us with a deeper understanding of the nature of computation and representation. For example,
This paper proposes a method called implicit encapsulation for creating layered stimulus models for use in constrained random verification environments. The proposed method relies heavily on the VMM class library – the atomic generator and scenario
By Zhai Chengxiang The purpose of this survey is to systematically and critically review the existing work in applying statistical language models to information retrieval, summarize their contributions, and point out outstanding challenges.
