Abstract: This article proposes a method and algorithm to automatically generate the propagation supermatrix (PSM) and scattering supermatrix (SSM) from a desired topology network in order to ...

Comprehensive notes and solved problem sets from Casella-Berger: Statistical Inference (Chapters 6-9), covering essential concepts in Statistical Inference. Topics include Sufficiency, Point ...

Abstract: We study the recursive EM algorithm with adaptive step size (REMA) in this work. Recursive EM is a stochastic approximation procedure for finding the maximum likelihood (ML) estimate.

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA. Compositional data exclusively consists of relative information. These entities are part of a broader entity.

ABSTRACT: Maximum likelihood (ML) estimation for the generalized asymmetric Laplace (GAL) distribution also known as Variance gamma using simplex direct search algorithms is investigated. In this ...

Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, United States Center for Computational Biology, Flatiron Institute, New York, New York 10010, United States Article ...

Cryoelectron microscopy is an imaging technique for determining molecular structures from randomly oriented projection images, with important applications in basic science and drug design. A large ...

The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their ...

My first introduction to LinkedIn was in my senior year of college, when it was a requirement of my Career Preparation class to complete a LinkedIn profile. I remember putting up a recent selfie ...