# Overview

In my previous blogpost, I motivated the EM algorithm in the context of estimating the parameters of a two-component Gaussian mixture density. In this case, we can write the estimators of the mixing probability, means, and variance in a nice closed form, and I demonstrated how to implement the corresponding iterative estimation procedure from scratch. Results were then compared to those obtained from the very nice R package `flexmix`

.

However, rarely do we get such nice closed form estimators! We usually need to use numerical methods to maximize our objective function directly. In this blog post, I demonstrate how we can specify our objective function, and use the `optim`

function in R to obtain our parameter estimates. `optim`

has lots of options, and we will cover how to change the optimization procedure and implement restrictions on our parameter spaces.

## EM for two-component Gaussian mixture

Let’s quickly recap our motivation, previously discussed in Embracing the EM algorithm: One continuous response.

We randomly sample patients from a population, and examine the empirical density of their responses. We notice two modes, and based on prior knowledge, hypothesize that the density is actually a mixture of two Gaussian densities. For example, the density centered around greater responses may correspond to “healthy” patients and the other to “ill” patients. We would like to (1) estimate the probability of belonging to each subpopulation; and (2) estimate subpopulation parameters, e.g. mean response in among the healthy and ill. But, we have a problem: we don’t know who belongs to each subpopulation. In other words, subpopulation labels are unobserved or “latent.”

We can represent the density of the observed responses as a mixture of the subpopulation densities:

That is, individuals belong to the first subpopulation, or are distributed according to density , according to probability and to the second, distributed per , with probability . We assume both densities are Gaussian with respective means and and variances and .

Continue reading EM Algorithm Essentials: Maximizing objective functions using R’s optim