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Know what a Gibbs sampler is, and a mixture model is, and be familiar with Bayesian inference for a normal mean and for the two class problem.


We consider using Gibbs sampling to perform inference for a normal mixture model, \[X_1,\dots,X_n \sim f(\cdot)\] where \[f(\cdot) = \sum_{k=1}^K \pi_k N(\cdot; \mu_k,1).\] Here \(\pi_1,\dots,\pi_K\) are non-negative and sum to 1, and \(N(\cdot;\mu,\sigma^2)\) denotes the density of the \(N(\mu,\sigma^2)\) distribution.

Recall the latent variable representation of this model: \[\Pr(Z_j = k) = \pi_k\] \[X_j | Z_j = k \sim N(\mu_k,1)\]

To illustrate, let’s simulate data from this model:


# generate from mixture of normals
#' @param n number of samples
#' @param pi mixture proportions
#' @param mu mixture means
#' @param s mixture standard deviations
rmix = function(n,pi,mu,s){
  z = sample(1:length(pi),prob=pi,size=n,replace=TRUE)
  x = rnorm(n,mu[z],s[z])
x = rmix(n=1000,pi=c(0.5,0.5),mu=c(-2,2),s=c(1,1))

Version Author Date
c3b365a John Blischak 2017-01-02

Gibbs sampler

Suppose we want to inference for the parameters \(\mu,\pi\). That is, we want to sample from \(p(\mu,\pi | x)\). We can use a Gibbs sampler. However, to do this we have to augment the space to sample from \(p(z,\mu,\pi | x)\), not only \(p(\mu,\pi | x)\).

Here is the algorithm in outline:

  • sample \(\mu\) from \(\mu | x, z, \pi\)
  • sample \(\pi\) from \(\pi | x, z, \mu\)
  • sample \(z\) from \(z | x, \pi, \mu\)

The point here is that all of these conditionals are easy to sample from.


  normalize = function(x){return(x/sum(x))}
  #' @param x an n vector of data
  #' @param pi a k vector
  #' @param mu a k vector
  sample_z = function(x,pi,mu){
    dmat = outer(mu,x,"-") # k by n matrix, d_kj =(mu_k - x_j)
    p.z.given.x = as.vector(pi) * dnorm(dmat,0,1) 
    p.z.given.x = apply(p.z.given.x,2,normalize) # normalize columns
    z = rep(0, length(x))
    for(i in 1:length(z)){
      z[i] = sample(1:length(pi), size=1,prob=p.z.given.x[,i],replace=TRUE)
  #' @param z an n vector of cluster allocations (1...k)
  #' @param k the number of clusters
  sample_pi = function(z,k){
    counts = colSums(outer(z,1:k,FUN="=="))
    pi = gtools::rdirichlet(1,counts+1)

  #' @param x an n vector of data
  #' @param z an n vector of cluster allocations
  #' @param k the number o clusters
  #' @param prior.mean the prior mean for mu
  #' @param prior.prec the prior precision for mu
  sample_mu = function(x, z, k, prior){
    df = data.frame(x=x,z=z)
    mu = rep(0,k)
    for(i in 1:k){
      sample.size = sum(z==i)
      sample.mean = ifelse(sample.size==0,0,mean(x[z==i]))
      post.prec = sample.size+prior$prec
      post.mean = (prior$mean * prior$prec + sample.mean * sample.size)/post.prec
      mu[i] = rnorm(1,post.mean,sqrt(1/post.prec))
  gibbs = function(x,k,niter =1000,muprior = list(mean=0,prec=0.1)){
    pi = rep(1/k,k) # initialize
    mu = rnorm(k,0,10)
    z = sample_z(x,pi,mu)
    res = list(mu=matrix(nrow=niter, ncol=k), pi = matrix(nrow=niter,ncol=k), z = matrix(nrow=niter, ncol=length(x)))
    for(i in 2:niter){
        pi = sample_pi(z,k)
        mu = sample_mu(x,z,k,muprior)
        z = sample_z(x,pi,mu)
        res$mu[i,] = mu
        res$pi[i,] = pi
        res$z[i,] = z

Try the Gibbs sampler on the data simulated above. We see it quickly moves to a part of the space where the mean parameters are near their true values (-2,2).

  res = gibbs(x,2)

Version Author Date
c3b365a John Blischak 2017-01-02

If we simulate data with fewer observations we should see more uncertainty

  x = rmix(100,c(0.5,0.5),c(-2,2),c(1,1))
  res2 = gibbs(x,2)

Version Author Date
c3b365a John Blischak 2017-01-02

And fewer observations still…

  x = rmix(10,c(0.5,0.5),c(-2,2),c(1,1))
  res3 = gibbs(x,2)

Version Author Date
c3b365a John Blischak 2017-01-02

And we can get credible intervals (CI) from these samples (discard the first few samples as “burn-in”).

For example, to get 90% posterior CIs for the mean parameters:

       5%       95% 
-2.644896 -1.004009 
       5%       95% 
0.9400428 2.7773584 

R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.2.0 Rcpp_1.0.0      gtools_3.8.1    digest_0.6.18  
 [5] rprojroot_1.3-2 backports_1.1.3 git2r_0.24.0    magrittr_1.5   
 [9] evaluate_0.12   stringi_1.2.4   fs_1.2.6        whisker_0.3-2  
[13] rmarkdown_1.11  tools_3.5.2     stringr_1.3.1   glue_1.3.0     
[17] xfun_0.4        yaml_2.2.0      compiler_3.5.2  htmltools_0.3.6
[21] knitr_1.21     

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