Penaksiran Parameter dan pada Distribusi Normal Menggunakan Metode Bayes dan Maksimum Likelihood Μ2σ

Abstract

The method of maximum likelihood inference based on the sample, and this method is also one way to assess the normal distribution, the basic idea is to find the maximum likelihood method of parameter values which give the possibility (likelihood) that most large to obtain the observed data as an estimator and its uses to determine the parameters that maximize the likelihood of the sample data. Maximum likelihood estimator is: ),x(f),...,x(f),,x(f)(Ln21θθθθ But if the population distribution is unknown then the maximum likelihood method can not be used. Bayes introduces a method which needs to know the form of initial distributions (priors) of the population known as the Bayes method. Before pulling a sample from a population sometimes obtained information about the parameters to be estimated. This information is then combined with information from the sample to be used in estimating population parameters. Bayes estimator is: f(θ|x1, x2, …, xn) = )x , ,x ,g(x ) :x ,… ,x ,f(xn21n21…