Title: The application of Monte Carlo methods for learning generalized linear model,
Abstract: Monte Carlo method is a broad class of computational algorithms that rely on
repeated random sampling to obtain numerical results. They are often used in
physical and mathematical problems and are most useful when it is difficult or
impossible to use other mathematical methods. Basically, many statisticians
have been increasingly drawn to Monte Carlo method in three distinct problem
classes: optimization, numerical integration, and generating draws from a
probability distribution. In this paper, we will introduce the Monte Carlo
method for calculating coefficients in Generalized Linear Model(GLM),
especially for Logistic Regression. Our main methods are Metropolis
Hastings(MH) Algorithms and Stochastic Approximation in Monte Carlo
Computation(SAMC). For comparison, we also get results automatically using MLE
method in R software.