It’s a classification algorithm, that is used where the response variable is categorical. The idea of Logistic Regression is to find a relationship between features and probability of particular ...

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort ...

Implementing Bayesian logistic regression is not required. If you can implement it independently, you can get a huge bonus (up to an extra 50%, since it has never been implemented before). To earn ...

Contributed by Emmanuel J. Candès, September 19, 2018 (sent for review June 21, 2018; reviewed by Nancy M. Reid and Huibin [Harry] Zhou) ...

Abstract: The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of ...

Abstract: In this letter, we consider the problem of field estimation using binary measurements. Previous work has formulated the problem as a parameter estimation problem, which can be solved in an ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...