SAMPLING FROM A RING DISTRIBUTION IN THREE DIMENSIONS I will here demonstrate a variety of Markov chain sampling methods using as an example a distribution that forms a ring in a space of three dimensions, parameterized by variables called "x", "y", and "z". For details on the various methods described, see mc-spec.doc. A table comparing the performance of the methods on these examples is included at the end of this section. Specifying the distribution. The "ring" distribution is specified by a 'dist-spec' command such as the following (the "\" says the command continues on the next line): > dist-spec glog \ "x^2/2 + y^2/2 + z^2/2 + (x+y+z)^2 + 10000/(1+x^2+y^2+z^2)" Recall that the formula given to 'dist-spec' is the "energy function", which is minus the log probability density, plus any constant. If the energy function above consisted of only the first three terms, the distribution would be multivariate normal, with x, y, and z being independent, each having mean zero and variance one. The fourth term, (x+y+z)^2, leaves the distribution still normal, but squashes it in the direction where x, y, and z increase equally. The final term is large near the origin, and from there decreases to zero symmetrically in all directions. It has the effect of making a hole in the centre of what would otherwise have been a normal distribution, leaving a ring shape. The examples below are assumed to start with a 'dist-spec' command of the above form, except that "glog" is replaced with the name of the log file used for the method being demonstrated. Simultaneous Metropolis updates. We will first see how to sample from this distribution using a variation of the Metropolis algorithm in which all three state variables are changed simultaneously. In the proposal distribution used, new values for the state variable are chosen independently, each from a normal distribution with mean equal to the present value, and a specified standard deviation. The following command specifies that 50 Metropolis operations of this sort should be done for each full iteration: > mc-spec glog.met,1 repeat 50 metropolis 1 end Here, "glob.met,1" is the name of the log file to use, which would have been created using a 'dist-spec' command like the one above. The "repeat 50 ... end" construction causes the enclosed operations to be repeated the given number of times. This saves space in the log file, compared to doing one metropolis operation for 50 times as many iterations. This is appropriate when many such operations will be needed to get to a substantially different point. A similar result can be obtained using an extra argument to 'dist-mc' (see xxx-mc.doc), but using "repeat" has the advantage that one can also easily look at the rejection rate over the 50 repetitions. We can now sample for 500 Markov chain iterations (a total of 25,000 metropolis updates) with the following command: > dist-mc glog.met,1 500 This takes about two seconds on our machine. We can look at what happened to the three state variables during these 500 iterations with a command such as > dist-plt t xyz glog.met,1 | plot Depending on your plot program's requirements, you might instead use a command such as > dist-tbl txyz glog.met,1 | plot or you might have to plot the variables one at a time, with commands such as > dist-plt t x glog.met,1 | plot From these plots, you can see that the chain quite rapidly reached the equilibrium distribution - maybe even by the end of the first iteration (ie, within the first 50 metropolis updates). Just to be sure, however, let's discard the first 10 iterations as "burn-in". We can now take a look at the "ring" distribution with commands like > dist-plt x y glog.met,1 10: | plot-points If "plot-points" plots points rather than lines, this will produce a scatterplot of the distribution for "x" and "y" (with "z" ignored), which will look like a flattened ring. The ring is actually circular, but it's tilted with respect to the axes, so you'll be able to see it as a circle only if you have a three-dimensional plotting program. However, the plot above will probably not show points distributed perfectly uniformly around the ring. Instead, there will be clumps here or there, which result from inadequate sampling. The 591 points plotted are not independent, as can also be seen from the plots of "x", "y", and "z" versus "t". To get a sample of points that are a good representation of the distribution using this chain, it would need to be run for more iterations. We can get a quantitative idea of how poor the sampling is with the following command: > dist-tbl x glog.met,1 10: | series mac 30 Number of realizations: 1 Total points: 491 Mean: -1.069628 S.E. from correlations: 1.343061 Lag Autocorr. Cum. Corr. 1 0.930028 2.860055 2 0.875426 4.610908 3 0.817085 6.245078 4 0.765703 7.776485 5 0.723927 9.224339 6 0.686476 10.597291 7 0.655353 11.907996 8 0.625940 13.159877 9 0.590206 14.340288 10 0.554448 15.449185 11 0.517685 16.484555 12 0.481981 17.448517 13 0.440608 18.329732 14 0.391103 19.111937 15 0.342534 19.797005 16 0.305640 20.408286 17 0.258187 20.924660 18 0.220043 21.364747 19 0.186663 21.738073 20 0.160253 22.058580 21 0.130252 22.319085 22 0.104981 22.529047 23 0.084475 22.697997 24 0.065285 22.828567 25 0.052381 22.933329 26 0.045782 23.024892 27 0.033173 23.091238 28 0.022730 23.136698 29 0.015462 23.167623 30 0.005316 23.178255 The 'dist-tbl' command above outputs a list of values for "x" for iterations from 10 on. The 'series' command with options "mac" finds the mean of these numbers, their autocorrelations (out to lag 30 here), and the cumulative correlations. The cumulative correlation at the earliest lag past which the autocorrelations are about zero indicates the factor by which sampling is made inefficient by the correlations (here, about 23); see Ex-dist-n.doc for more details. From symmetry, we know that the true mean for "x" is zero. The estimate of -1.069628 above is consistent with this, in view of the estimated standard error of +-1.343061. We can also get estimates using 'dist-est', but as discussed in Ex-dist-n.doc, the standard errors it produces do not account for autocorrelation. We might try to improve the efficiency of sampling by changing the standard deviation of the Metropolis proposal distribution - which is also known as the "stepsize" for the operation. One indication of whether the stepsize is appropriate is the rejection rate for the Metropolis operations, which can be viewed with a command such as > dist-plt t r glog.met,1 | plot Here, the rejection rate is about 0.7, which is acceptable. Very low or very high rejection rates are usually an indication that sampling would work better with a different stepsize. Although the stepsize of 1 that was used above appears to be OK, we could try a smaller stepsize with the following commands (following a 'dist-spec' command): > mc-spec glog.met,0.2 repeat 50 metropolis 0.2 end > dist-mc glob.met,0.2 500 Or we might try a larger stepsize: > mc-spec glog.met,5 repeat 50 metropolis 5 end > dist-mc glob.met,5 500 If enough iterations are done, the same estimates should be obtained all these chains, but some stepsizes will produce a more efficient chain than others. By examining plots of how the state variables change, and looking at the autocorrelations with 'series', one can conclude that sampling is much less efficient with a stepsize of 0.2 than with a stepsize of 1 (about five times less efficient, based on cumulative correlations). With a stepsize of 5, the sampling is about as good as with a stepsize of 1, even though the rejection rate is quite high. This is a phenomenon that occurs only in problems with an effective dimensionality of three or less - for higher-dimensional problems, a rejection rate close to one is generally an indication of poor sampling. Single-variable Metropolis updates. We can also try sampling using Metropolis updates that change only one variable at a time. This is done using "met-1" operations, specified as follows: > mc-spec glog.met1,1 repeat 18 met-1 1 end As with "metropolis" operations, we specify a "stepsize", which is the standard deviation for proposed change to a variable. Each "met-1" operation tries to change each variable in turn, accepting or rejecting the change based on the change in energy as a result of making the proposed change to just that variable. Since there are three state variables for this distribution, a single "met-1" operation must therefore calculate the energy three times, and hence takes about three times as long as a "metropolis" operation. To facilitate comparisons, the repeat count is corresponding less in this specification. As before, can now sample for 500 iterations using a 'dist-mc' command: > dist-mc glog.met1,1 500 On our machine, this also takes about two seconds (as will all such commands in this section). You can see how well this method samples in the same ways as discussed above. You could also try sampling using "met-1" with a stepsize of 0.2 and 5. You should see that the rejection rate with "met-1" is lower than with "metropolis" operations using the same stepsize. Nevertheless, sampling from this distribution seems to be less efficient with "met-1" than with "metropolis", drastically so when the stepsize is 0.2 or 5. This is not always so, however. For distributions where at least some of the variables are close to being independent, updating one variable at a time can be more efficient. It is also sometimes possible to save computation time when recomputing the energy after a change to just one variable, though that possibility is not presently exploited by this software. Single-variable slice sampling. Variables can also be updated one at a time is using single-variable slice sampling, which is described in my tech report on "Markov chain Monte Carlo methods based on `slicing' the distribution" (available from my web page). Several variations on this procedure are implemented in this software. The method in which the slice is found by "stepping out" can be done as follows: > mc-spec glog.slc1,1 repeat 4 slice-1 1 end > dist-mc glog.slc1,1 500 This does single-variable slice sampling using an initial interval of size 1, which is extended in steps of the same size until both ends are outside the slice. The "doubling" procedure is also implemented, but is not illustrated here. The "e" quantity records the average number of energy function evaluations done in the slice sampling updates for one iteration. We can find the average of this quantity over all iterations with a command such as > dist-tbl e glog.slc1,1 | series m Number of realizations: 1 Total points: 500 Mean: 5.406000 Note that 5.406 is the average number of evaluations for updating one variable, not for updating all three of them. As with the Metropolis methods, performance varies with the stepsize chosen. However, one advantage of single-variable slice sampling is that it is a bit less sensitive to the choice of stepsize than the single-variable Metropolis algorithm. Sampling with Hamiltonian dynamics. It is possible to sample much more efficiently by suppressing the random walk behaviour that the methods above all exhibit. One way of doing this is by adding "momentum" variables, which will keep the state moving in the same direction for an extended period of time. The original "position" variables along with these "momentum" variables can be updated by applying Hamiltonian dynamics for some period of fictitious time, implemented by performing some number of "leapfrog" steps with a specified stepsize. To produce an ergodic Markov chain, the momentum should also be changed using "heatbath" updates, but it should not be changed too quickly, as this will re-introduce random walk behaviour. For a detailed discussion of this "stochastic dynamics" method, see my book, Bayesian Learning for Neural Networks, or my review paper, Probabilistic Inference Using Markov Chain Monte Carlo Methods. We can try out this dynamical method as follows: > mc-spec glog.dyn,0.3 repeat 40 heatbath 0.98 dynamic 1 0.3 end > dist-mc glog.dyn,0.3 500 The argument of "heatbath" is the factor by which to multiply the old momentum variables (after which noise is added). A value of 1-d results in random walks being suppressed for around 1/d iterations. If you now look at the state variables with a command such as > dist-plt t xyz glog.dyn,0.3 | plot you will see that they are initially far from their stationary distribution, but after about 50 iterations they settle down, and from there on the chain samples very well. The initial behaviour of the chain can be understood by looking at what is happening to the the "kinetic energy", which is half the sum of squares of the momentum variables: > dist-plt t K glog.dyn,0.3 | plot Initially, the kinetic energy (as well as the "potential" energy, which is what is specified in 'dist-spec') is very large. It is only slowly dissipated, as a result of the "heatbath" updates of the momentum variables. Eventually, however, the kinetic energy reaches its equilibrium distribution (around a value of 3/2), and the chain samples from approximately the desired distribution. This method is not exact, however, because the Hamiltonian dynamics is simulated inexactly, biasing the results. The hybrid Monte Carlo method eliminates this bias by a using an acceptance test. Hybrid Monte Carlo. Several variations of the hybrid Monte Carlo method are supported by this software. In the "standard" method, each iteration starts by picking completely new values for the momentum variables with the "heatbath" operation. Hamiltonian dynamics is then simulated for some number of leapfrog steps, using some specified stepsize, and the end-point of the simulated trajectory is accepted or rejected based on the change in the total energy. The following commands apply the standard hybrid Monte Carlo method, using trajectories of 45 leapfrog steps, done with a stepsize of 0.3. To avoid problems with large initial energies, a few Metropolis updates are done at the beginning. > mc-spec glog.hmc,0.3 repeat 50 metropolis 1 > dist-mc glog.hmc,0.3 1 > mc-spec glog.hmc,0.3 heatbath hybrid 45 0.3 > dist-mc glog.hmc,0.3 500 The length of the trajectory should be chosen based on the number of steps for which we want to suppress random walks - longer for more difficult problems where it takes many steps to get from one end of the distribution to the other. As with the Metropolis methods, you can check the rejection rate with a command such as the following (the 2: causes the first iteration, which consisted of Metropolis updates, to be excluded): > dist-tbl r glog.hmc,0.3 2: | series m Number of realizations: 1 Total points: 500 Mean: 0.042084 It is also useful to look at the changes in energy on which the decisions to accept or reject were made: > dist-plt t D glog.hmc,0.3 2: | plot-points If most of these points are much greater than one, the rejection rate will be high. With a stepsize of 0.3, the change in energy is seldom greater than 0.5, but if the stepsize is increased to 0.8 much larger changes are often seen, and with a stepsize of 1, almost no trajectories are accepted. We can see how well the chain is sampling by plotting the state variables, as described above, and by looking at the autocorrelations, with a command such as: > dist-tbl x glog.hmc,0.3 10: | series mac 10 Number of realizations: 1 Total points: 491 Mean: 0.588512 S.E. from correlations: 0.437857 Lag Autocorr. Cum. Corr. 1 0.406767 1.813534 2 0.141586 2.096707 3 0.036505 2.169716 4 0.002760 2.175236 5 -0.001138 2.172961 6 0.031450 2.235860 7 -0.005979 2.223903 8 -0.006971 2.209960 9 -0.022040 2.165880 10 -0.064142 2.037595 From the cumulative correlations, we can estimate that estimating the expectation of "x" using points from this chain is a factor of only about 2.2 less efficient than using independent points. This is about ten times better than the best of the chains described above. On the other hand, we have seen that some care is needed to pick an appropriate stepsize for hybrid Monte Carlo, and it is also often necessary to start at a reasonably good point, as was done here by doing a few Metropolis updates at the beginning. The "persistent" form of hybrid Monte Carlo (described in Bayesian Learning for Neural Networks) is also implemented. Here are the appropriate commands: > mc-spec glog.phmc,0.3 repeat 50 metropolis 1 > dist-mc glog.phmc,0.3 1 > mc-spec glog.phmc,0.3 repeat 35 heatbath 0.99 hybrid 1 0.3 negate end > dist-mc glog.phmc,0.3 500 The use of a "heatbath" operation with a non-zero "decay" causes the momentum to change only slowly. Because of this, even though only a single leapfrog update is done, random walk behaviour will still be suppressed. The "negate" operation negates the momentum, which normally undoes a negation at the end of the "hybrid" operation. If the "hybrid" update was rejected, however, the first negation will not have been done, and movement will be reversed, undermining the random walk suppression. To avoid this, a smaller stepsize is used, to keep the rejection rate very small. Reflective slice sampling. Another way of avoiding random walks is to apply slice sampling to all variables at once, and to sample from within the multivariate slice by a sort of dynamical method that proceeds at a constant speed while reflecting off the boundaries of the slice. We would rather not compute these boundaries exactly, however. Instead, we can proceed in steps of some size and when we cross the boundary reflect at either the last inside point or the first outside point. These reflection operations are based on the gradient of the energy at that point. Slice sampling with reflection from the last inside point is done as follows: > mc-spec glog.slci,0.3 heatbath slice-inside 35 0.3 > dist-mc glog.slci,0.3 500 The "heatbath" operation picks a random momentum vector, which sets the speed of motion and the initial direction. We then find the slice and take 35 steps within the slice with that velocity, using a stepsize of 0.3 (ie, the change in the state at each step is 0.3 times the momentum vector). If a step takes us from inside the slice to outside the slice, we backtrack, and change the momentum vector by reflection based on the gradient. It is necessary to check that this reflection would also occur in the reversed trajectory; if not, we must reject by negating the momentum, causing further steps to retrace the trajectory. Rejections will be less frequent if the stepsize is small. Slice sampling with outside reflection is done as follows: > mc-spec glog.slco,0.3 heatbath slice-outside 45 0.3 > dist-mc glog.slco,0.3 500 This method operates as described for inside reflection, except that when a step ends at a point outside the slice, we stay there, but reflect based on the gradient at that point. If the final point is inside the slice, we accept it. Otherwise we reject, and return to the point from which the trajectory started. Both inside and outside reflection work reasonably well for this distribution - not quite as good as hybrid Monte Carlo, but better than the other methods. Overrelaxed slice sampling. Random walks can also be suppressed for some distributions using single-variable slice sampling with "overrelaxation", in which variables are changed from their current point to the corresponding point on the "other side" of their conditional distribution. This can be done with the following commands: > mc-spec glog.slcv,0.3 slice-over 8 0.1 0.3 > dist-mc glog.slcv,0.3 500 The first argument of "slice-over" is the number of "refinements" used to more accurately pin down the end-points of the slice (using bisection). More refinements result in an update that is closer to an ideal overrelaxed update, and also reduces the probability of rejection (caused by the overrelaxed point being outside the slice). The second argument specifies the probability of doing an ordinary single-variable slice sampling update rather than an overrelaxed one. Occasional ordinary updates are necessary in order to ensure ergodicity, but they should not be done too often if random walks are to be suppressed. The final argument is the stepsize to use when finding the endpoints of the slice initially. For this distribution, overrelaxation does not work as well as the dynamical methods or reflective slice sampling, but it is effective for some other distributions. Performance summary. The following table summarizes the inefficiency factor for estimating the expectation of the "x" variable using the various methods, determined from estimates of the cumulative correlations. The repetition counts for the methods were set so that they all required about 2 seconds of computation time for 500 iterations. METHOD STEPSIZE CUM.CORR. NOTES metropolis 0.2 122 1 23 5 20 met-1 0.2 460 Cum. corr. estimated using 2000 iterations 1 55 5 118 slice-1 0.2 77 Computation times had to be a bit greater 1 32 than 2s for the slice sampling methods, as 5 62 the repetition count has to be an integer dynamic 0.3 3.3 This method is not exact hybrid (HMC) 0.3 2.2 persistent HMC 0.18 9 slice-inside 0.3 11 slice-outside 0.3 4 slice-over 2 28 The relative performance of the different methods will of course be somewhat different for different distributions, but large gains from using hybrid Monte Carlo (HMC) or other methods that suppress random walks are common.