Parallel Tempering

Setup

For this example we will start by writing the likelihood and prior functions in C++. If this sounds unfamiliar to you, check out the earlier vignette for a simple example. Our basic model will assume that our data are normally distributed with mean alpha^2*beta. The alpha^2 term here means that both positive and negative values of alpha map to the same value, thereby creating a multimodal distribution. The *beta term ensures that alpha and beta are highly correlated. We will also use a third parameter epsilon to represent some random noise that we want to integrate over. While this is a highly contrived example, it does have the advantage of being horrendously awkward!

For our prior we assume that alpha is uniform [-10,10], beta is uniform [0,10], and epsilon is normally distributed with mean 0 and standard deviation 1.

#include <Rcpp.h>
using namespace Rcpp;

// [[Rcpp::export]]
SEXP loglike(Rcpp::NumericVector params, Rcpp::List data, Rcpp::List misc) {
  
  // unpack data
  std::vector<double> x = Rcpp::as< std::vector<double> >(data["x"]);
  
  // unpack parameters
  double alpha = params["alpha"];
  double beta = params["beta"];
  double epsilon = params["epsilon"];
  
  // sum log-likelihood over all data
  double mean = alpha*alpha*beta + epsilon;
  double ret = 0.0;
  for (unsigned int i = 0; i < x.size(); ++i) {
    ret += R::dnorm(x[i], mean, 1.0, true);
  }
  
  // return as SEXP
  return Rcpp::wrap(ret);
}

// [[Rcpp::export]]
SEXP logprior(Rcpp::NumericVector params, Rcpp::List misc) {
  
  // unpack parameters
  double epsilon = params["epsilon"];
  
  // calculate logprior
  double ret = -log(20.0) - log(10.0) + R::dnorm(epsilon, 0.0, 1.0, true);
  
  // return as SEXP
  return Rcpp::wrap(ret);
}

// [[Rcpp::export]]  
SEXP create_xptr(std::string function_name) {  
  typedef SEXP (*funcPtr_likelihood)(Rcpp::NumericVector params, Rcpp::List data, Rcpp::List misc);  
  typedef SEXP (*funcPtr_prior)(Rcpp::NumericVector params, Rcpp::List misc);  
  
  if (function_name == "loglike"){
    return(Rcpp::XPtr<funcPtr_likelihood>(new funcPtr_likelihood(&loglike)));
  } 
  if (function_name == "logprior"){
    return(Rcpp::XPtr<funcPtr_prior>(new funcPtr_prior(&logprior)));
  } 
  
  stop("cpp function %i not found", function_name);
}

As always, we need to define a dataframe of parameters so that drjacoby knows what parameter ranges to expect:

# define parameters dataframe
df_params <- define_params(name = "alpha", min = -10, max = 10, init = 5,
                           name = "beta", min = 0, max = 10, init = 5,
                           name = "epsilon", min = -Inf, max = Inf, init = 0)

Finally, we need some data. For simplicity we will use a series of values drawn from a normal distribution with mean 10. These are obviously not drawn from the true model, but will have the advantage of creating a horribly awkward posterior.

# draw example data
data_list <- list(x = rnorm(100, mean = 10))

Running the MCMC

First, we will try running the basic MCMC without parallel tempering. The following block of code repeats the same MCMC analysis nine times, each time producing a plot of posterior alpha against beta:

plot_list <- list()
for (i in seq_len(9)) {
  
  # run MCMC
  mcmc <- run_mcmc(data = data_list,
                   df_params = df_params,
                   loglike = "loglike",
                   logprior = "logprior",
                   burnin = 1e3,
                   samples = 1e4,
                   chains = 1,
                   silent = TRUE)
  
  # create plot of alpha against beta
  plot_list[[i]] <- plot_scatter(mcmc, "alpha", "beta") +
    ggplot2::ylim(0, 10) + ggplot2::xlim(-10, 10) + ggplot2::theme(legend.position = "none")
}

# plot grid
gridExtra::grid.arrange(grobs = plot_list)

Clearly this MCMC is not mixing well! By looking over all nine plots we can get a rough idea of what the distribution should look like, but no single MCMC run has captured it adequately. You can experiment with increasing the number of samples - you should get better results for more samples, but a very large number of samples are needed before we get good mixing between the left and right sides of the distribution.

Parallel tempering is perfectly designed to solve this kind of problem. To activate parallel tempering we need to specify an additional argument to the run_mcmc() function - the number of rungs. Each rung represents a different MCMC chain arranged in a “temperature ladder”, with the hotter chains being more free to explore the parameter space. Every iteration, chains have the option of swapping parameter values between rungs, thereby allowing the freely-moving hot chains to propose values to the more constrained colder chains. When looking at the output, we tend to focus exclusively on the coldest chain which can be interpreted the same way as a single chain from regular MCMC.

In this example we use 20 rungs:

# run MCMC
mcmc <- run_mcmc(data = data_list,
                 df_params = df_params,
                 loglike = "loglike",
                 logprior = "logprior",
                 burnin = 1e3,
                 samples = 1e4,
                 rungs = 20,
                 chains = 1,
                 pb_markdown = TRUE)
#> MCMC chain 1
#> burn-in
#>   |                                                                              |======================================================================| 100%
#> acceptance rate: 42.5%
#> sampling phase
#>   |                                                                              |======================================================================| 100%
#> acceptance rate: 44%
#> chain completed in 1.518685 seconds
#> total MCMC run-time: 1.52 seconds

# create plot of alpha against beta
plot_scatter(mcmc, "alpha", "beta") +
    ggplot2::ylim(0, 10) + ggplot2::xlim(-10, 10) + ggplot2::theme(legend.position = "none")

You should see a much better characterisation of the posterior distribution. The run time scales approximately linearly with the number of rungs, so there is a computational cost to using this method, but on the other hand our results are far better than we would obtain by simply increasing the number of samples by a factor of 20.

How many rungs to use?

Parallel tempering in drjacoby works by proposing swaps between adjacent rungs in the temperature ladder, which are accepted or rejected according to the standard Metropolis-Hastings ratio. If, for example, a hot rung happens upon a set of parameter values that have high likelihood then there is a good chance these values will be swapped up to the next rung. In this way, information can effectively bubble-sort its way from the prior (the hottest chain) to the posterior (the coldest chain). The average chance of a swap being accepted depends on the two adjacent rungs being similar enough in distribution that values drawn from one have a high likelihood in the other. This, in turn, means that swaps have a higher chance of being accepted when we have a large number of densely packed rungs.

To explore this, we start by re-running the above MCMC with a small number of rungs and a small number of samples to highlight the problem. We can use the plot_mc_acceptance() function to plot the acceptance rate between rungs:

# run MCMC
mcmc <- run_mcmc(data = data_list,
                 df_params = df_params,
                 loglike = "loglike",
                 logprior = "logprior",
                 burnin = 1e3,
                 samples = 1e3,
                 rungs = 10,
                 chains = 1,
                 silent = TRUE)

# plot coupling acceptance rates
plot_mc_acceptance(mcmc, x_axis_type = 2)

The 10 vertical grey bars in the above plot show the positions of our 10 rungs in the thermodynamic interval [0,1], and the red dots between them show the proportion of swaps that were accepted between rungs (this information is available via mcmc$diagnostics$mc_accept). Notice that the acceptance rate between the hottest and second-hottest rung is close to zero, meaning very little of the information in the hottest rung made it up the ladder to the colder rungs. We can see the problems this causes when we plot the posterior draws:

plot_scatter(mcmc, "alpha", "beta") +
    ggplot2::ylim(0, 10) + ggplot2::xlim(-10, 10) + ggplot2::theme(legend.position = "none")

Although we have some mixing between the left and right sides of the distribution, overall sampling is still very patchy.

One way of improving mixing between rungs is simply to increase the number of rungs so the distance between distributions is smaller:

# run MCMC
mcmc <- run_mcmc(data = data_list,
                 df_params = df_params,
                 loglike = "loglike",
                 logprior = "logprior",
                 burnin = 1e3,
                 samples = 1e3,
                 rungs = 50,
                 chains = 1,
                 silent = TRUE)

# plot coupling acceptance rates
plot_mc_acceptance(mcmc, x_axis_type = 2)

# create plot of alpha against beta
plot_scatter(mcmc, "alpha", "beta") +
    ggplot2::ylim(0, 10) + ggplot2::xlim(-10, 10) + ggplot2::theme(legend.position = "none")

This has helped, as we now have acceptance rates going “deeper” towards the prior, however the hottest rung is still not passing information up the ladder. This strategy also comes at a high computational cost, as we are effectively running the MCMC algorithm 50 times. A more efficient method is usually to change the distribution of rungs so they are more concentrated at low thermodynamic powers (i.e. near the prior end of the spectrum). This can be achieved through the parameter alpha, which represents a power that the temperature ladder is raised to. Higher values of alpha therefore lead to rungs being more concentrated at the prior end of the spectrum. The previous two MCMCs had alpha set to 1.0 by default, leading to evenly distributed rungs between [0,1]. Now we repeat the MCMC with just 20 rungs and alpha = 5.0:

# run MCMC
mcmc <- run_mcmc(data = data_list,
                 df_params = df_params,
                 loglike = "loglike",
                 logprior = "logprior",
                 burnin = 1e3,
                 samples = 1e3,
                 rungs = 20,
                 chains = 1,
                 alpha = 5.0,
                 silent = TRUE)

# plot coupling acceptance rates
# we could also use the option x_axis_type = 1 if we wanted to see these
# points spread out evenly, rather than at their true position in the
# thermodynamic interval
plot_mc_acceptance(mcmc, x_axis_type = 2)

# create plot of alpha against beta
plot_scatter(mcmc, parameter1 = "alpha", "beta") +
    ggplot2::ylim(0, 10) + ggplot2::xlim(-10, 10) + ggplot2::theme(legend.position = "none")

We find that with just 20 rungs we now have decent acceptance rates all the way through the temperature ladder. If we really wanted to fine-tune the mixing we could use the beta_manual argument inside run_mcmc() to define the temperature ladder exactly as a vector of values between 0 and 1. Note, this ladder is always raised to the power alpha, so we should set alpha=1.0 if we don’t want any additional transformation applied.

In summary, the correct number of rungs to use in parallel tempered MCMC is whatever number provides decent acceptance rates all the way through our temperature ladder. When this is the case we can be condifent that the prior is “talking to” the posterior, making it extremely unlikely (but not impossible) that the MCMC is still missing large parts of the posterior distribution.