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See here for a PDF version of this vignette.

Prerequisites

You should be familiar with the Metropolis-Hastings algorithm, introduced here, and elaborated on here.

Caveat on code

Note that the code is designed to be readable by a beginner, rather than “efficient”. The idea is that you can use this code to learn about the basics of MCMC, but not as a model for how to program well in R!

Example 1: Sampling from an exponential distribution using MCMC

Any MCMC scheme aims to produce (dependent) samples from a “target” distribution. In this case we are going to use the exponential distribution with mean 1 as our target distribution. Here we define this function (on the log scale):

log_exp_target <- function (x)
  dexp(x,rate = 1,log = TRUE)

The following code implements a simple M-H algorithm. (Note that the parameter “log_target” is a function which computes the log of the target distribution; you may be unfamiliar with the idea of passing a function as a parameter, but it works just like any other type of parameter)

easyMCMC <- function (log_target, niter, startval, proposalsd) {
  x <- rep(0,niter)
  x[1] <- startval
  for (i in 2:niter) {
    currentx <- x[i-1]
    proposedx <- rnorm(1,currentx,proposalsd) 
    A <- exp(log_target(proposedx) - log_target(currentx))
    u <- runif(1)
    if(u < A)
      x[i] <- proposedx
    else
      x[i] <- currentx
  }
  return(x)
}

Now we run the MCMC three times from different starting points and compare the results:

par(mfrow = c(1,2))
z1 <- easyMCMC(log_exp_target,1000,3,1)
z2 <- easyMCMC(log_exp_target,1000,1,1)
z3 <- easyMCMC(log_exp_target,1000,5,1)
plot(z1,type = "l",xlab = "time",ylab = "x")
lines(z2,col = 2)
lines(z3,col = 3)
plot(log_exp_target(z1),xlab = "time",ylab = "log target")
lines(log_exp_target(z2),col = 2)
lines(log_exp_target(z3),col = 3)

Version	Author	Date
db49bb6	Peter Carbonetto	2026-01-23

par(mfrow = c(1,3))
maxz <- max(c(z1,z2,z3))
hist(z1,breaks = seq(0,maxz,length.out = 20),xlab = "x")
hist(z2,breaks = seq(0,maxz,length.out = 20),xlab = "x")
hist(z3,breaks = seq(0,maxz,length.out = 20),xlab = "x")

Version	Author	Date
db49bb6	Peter Carbonetto	2026-01-23

Exercise

Use the function “easyMCMC” to explore the following:

How do different starting values affect the MCMC scheme? (Try some extreme starting points.)
What is the effect of having a bigger (or smaller) proposal standard deviation? (Again, try some extreme values.)
Try changing the (log) target function to “log_target_bimodal” below. What does this target distribution look like? What happens if the proposal standard deviation is too small here? (e.g., try 1 and 0.1 and see what happens)

log_target_bimodal <- function (x)
  log(0.8*dnorm(x,-4,1) + 0.2*dnorm(x,4,1))

Example 2: Estimating an allele frequency

A standard assumption when modelling genotypes of biallelic loci (e.g., loci with alleles \(A\) and \(a\)) is that the population is “mating at random”. From this assumption, it follows that the population will be in “Hardy-Weinberg equilibrium” (HWE), which means that if \(p\) is the frequency of the \(A\) allele, then the genotypes \(AA\), \(Aa\) and \(aa\) have frequencies \(p^2\), \(2p(1-p)\) and \((1-p)^2\), respectively.

A simple prior for \(p\) is to assume it is uniform on \([0,1]\). Suppose that we sample \(n\) individuals, and observe \(n_{AA}\) individuals with genotype \(AA\), \(n_{Aa}\) individuals with genotype \(Aa\), and \(n_{aa}\) individuals with genotype \(aa\).

The following R code implements a M-H algorithm to sample from the posterior distribution of \(p\). Try to go through the code to see how it works.

The first function returns the (log) prior density:

log_prior <- function (p) {
  if((p < 0) || (p > 1))
    return(-Inf)
  else
    return(0)
}

The second function returns the (log) likelihood:

log_likelihood_hwe <- function (p, nAA, nAa, naa)
  return(2*nAA*log(p) +
         nAa*log(2*p*(1-p)) +
         2*naa*log(1-p))

And now this function implements the M-H algorithm:

psampler <- function (nAA, nAa, naa, niter, pstartval, pproposalsd) {
  p <- rep(0,niter)
  p[1] <- pstartval
  for (i in 2:niter) {
    currentp <- p[i-1]
    newp <- currentp + rnorm(1,0,pproposalsd)
    A <- exp(log_prior(newp)
             - log_prior(currentp)
             + log_likelihood_hwe(newp,nAA,nAa,naa)
             - log_likelihood_hwe(currentp,nAA,nAa,naa))
    if (runif(1) < A)
      p[i] <- newp
    else
      p[i] <- currentp
  }
  return(p)
}

Try running this algorithm for \(n_{AA} = 50\), \(n_{Aa} = 21\), \(n_{aa} = 29\):

z <- psampler(50,21,29,10000,0.5,0.01)

Now here’s some R code to compare the samples generated by the M-H algorithm with the theoretical posterior. In this case, the theoretical posterior is available analytically; since we observed \(A\) 121 times and \(a\) 79 times, the posterior for \(p\) is \(\mathrm{Beta}(121 + 1,79 + 1)\).

x <- seq(0,1,length.out = 1000)
hist(z,breaks = 32,probability = TRUE,xlab = "x",main = "")
lines(x,dbeta(x,122,80))

Version	Author	Date
db49bb6	Peter Carbonetto	2026-01-23

You might also like to discard the first 5,000 samples as “burnin”:

hist(z[5001:10000],breaks = 16,probability = TRUE,xlab = "x",main = "")
lines(x,dbeta(x,122,80))

Version	Author	Date
db49bb6	Peter Carbonetto	2026-01-23

Exercise

Investigate how the starting point and proposal standard deviation affect the convergence of the algorithm.

Example 3: Estimating an allele frequency and inbreeding coefficient

A slightly more complex alternative than HWE is to assume that there is a tendency for people to mate with others who are slightly more related than “random” (as might happen in a geographically structured population, for example). This will result in an excess of homozygotes compared with HWE. A simple way to capture this is to introduce an extra parameter, the “inbreeding coefficient” \(f\), and assume that the genotypes \(AA\), \(Aa\) and \(aa\) have frequencies \(fp + (1-f)p^2\), \(2(1-f) p(1-p)\), and \(f(1-p) + (1-f)(1-p)^2\), respectively.

In most cases, it would be natural to treat \(f\) as a feature of the population, and therefore assume \(f\) is constant across loci. For simplicity, we will consider just a single locus.

Note that both \(f\) and \(p\) are constrained to lie between 0 and 1 (inclusive). A simple prior for each of these two parameters is to assume that they are independent and uniform on \([0,1]\).

Suppose that we sample \(n\) individuals, and observe \(n_{AA}\) individuals with genotype \(AA\), \(n_{Aa}\) individuals with genotype \(Aa\), and \(n_{aa}\) individuals with genotype \(aa\).

Exercise

Write an M-H algorithm to sample from the joint distribution of \(f\) and \(p\). Hint: Below is some code to get you started. (You’ll need to fill in the “…”) As a first step, I suggest writing the function to compute the log-likelihood for the inbreeding model.

log_likelihood_inbreeding <- function (...) {
  # ...
}

fpsampler <- function (nAA, nAa, naa, niter, fstartval, pstartval,
                       fproposalsd, pproposalsd) {
  f <- rep(0,niter)
  p <- rep(0,niter)
  f[1] <- fstartval
  p[1] <- pstartval
  for (i in 2:niter) {
    currentf <- f[i-1]
    currentp <- p[i-1]
    # newf <- currentf + ...
    # newp <- currentp + ...
    # ...
  }
  return(list(f = f,p = p))
}

Use the output of your M-H algorithm to compute point estimates for \(f\) and \(p\) (e.g., posterior means) as well as interval estimates for \(f\) and \(p\) (e.g., 90% posterior credible intervals) when the data are \(n_{AA} = 50\), \(n_{Aa} = 21\), \(n_{aa} = 29\).

Addendum: Gibbs sampling

You could also tackle this problem with a Gibbs sampler (see also the gibbs1 and gibbs2 vignettes). To do so, you will want to use the following “latent variable” representation of the model: \[ \begin{aligned} \Pr(g_i = AA \mid z_i = 0) &= p^2 \\ \Pr(g_i = Aa \mid z_i = 0) &= 2p(1-p) \\ \Pr(g_i = aa \mid z_i = 0) &= (1-p)^2, \end{aligned} \] \[ \begin{aligned} \Pr(g_i = AA \mid z_i = 1) &= p \\ \Pr(g_i = Aa \mid z_i = 1) &= 0 \\ \Pr(g_i = aa \mid z_i = 1) &= 1-p, \end{aligned} \] and \[ z_i \sim \mathrm{Bernoulli}(f). \]

Note that summing over \(z_i\) gives the same model as above, e.g., \(\Pr(g_i = AA) = fp + (1-f)p^2\). (As an exercise, show that the probabilities of \(g_i = aa\) and \(g_i = Aa\) from this “latent variable augmented” model are the same as above.)

Reflection: What does the latent variable \(z_i\) represent?

Exercise

Using the latent variable representation above, implement a Gibbs sampler to generate samples from the joint distribution of \(z, f\) and \(p\) given the genotype data \(g\). Hint: this involves iterating the following steps:

Sample \(z\) from \(p(z \mid g, f, p)\).
Sample \(f, p\) from \(p(f, p \mid g, z)\).

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# [1] stats     graphics  grDevices utils     datasets  methods   base     
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Simple examples of the Metropolis-Hastings algorithm

Matthew Stephens

January 23, 2026

Prerequisites

Caveat on code

Example 1: Sampling from an exponential distribution using MCMC

Exercise

Example 2: Estimating an allele frequency

Exercise

Example 3: Estimating an allele frequency and inbreeding coefficient

Exercise

Addendum: Gibbs sampling

Exercise