Maximum Likelihood Estimation and the Newton-Raphson method
www.jorgelopezperez.com › posts › maximum-likelihoodApr 26, 2020 · newton_raphson <- function(xn, epsilon = 1e-6, n = 500) { # store all values values <- xn # loop n times (in this example we'll never reach the max number of iterations) for (i in seq_len(n)) { # NR equation xn1 <- xn - func(xn)/dfunc(xn) cat("Iteration", i, "Value", xn1, " ") # accumulative iteration values values <- c(values, xn1) # if difference between xn1 and xn is less than epsilon, convergence reached if(abs(xn1 - xn) < epsilon) { cat("Convergence reached!", " ") res <- list(final ...
linear algebra - Newton Method to find the Maximum value ...
math.stackexchange.com › questions › 3245123May 30, 2019 · That's because it depends a bit on which Newton method you refer to. In the one case, it's Newton's root-finding algorithm applied to the gradient of the function: this method will find a local extremum which may be a minimum or a maximum (or a saddle point). To find which, you need further exporation (for instance, looking at second order information or at the values of the function at different extrema).