Dynesty Guide

The Dynesty sampler is just one of the samplers available in bilby, but it is well-used and found to be fast and accurate. Here, we provide a short guide to its implementation. This will not be a complete guide, additional help can be found in the Dynesty documentation.

All of the options discussed herein can be set in the bilby.run_sampler() call. For example, to set the number of live points to 1000

>>> bilby.run_sampler(likelihood, priors, sampler="dynesty", nlive=1000)


Bilby checks the kwargs input through run_sampler. If you miss-spell a word, you will see a warning “Supplied argument X not an argument of dynesty, removing.”

Overview of dynesty

Like many nested samplers, dynesty operates on the unit cube with a prior_tranform function to transform a point in the unit cube to the target space. When it begins sampling, it draws samples from the unit cube (uniform sampling) until reaching the first_update criteria. As of v1.0.0, this defaults to

first_update = dict(min_ncall=2*nlive, min_eff=10)

That is, the first update happens when either it has called the likelihood function twice as many times as there are live points or the sampling efficiency hits 10%. You can change any of these by passing first_update as a dictionary to run_sampler.

Once the first update occurs, dynesty switches to the bounding method given by the keyword bound. After this, new points are proposed by taking an existing live point and sampling using the sample keyword. This continues until one of the stopping criteria are reached:

  1. The estimated remaining evidence is below the kwarg dlogz (default is 0.1)

  2. The effective number of samples exceeds the kwarg n_effective (default is 5000)

Bilby-specific implementation details

In Bilby, we have re-implemented the sample="rwalk" sample method (you can see exact details by looking at the function bilby.core.sampler.dynesty.sample_rwalk_bilby. In dynesty, this method took an argument walks which was the fixed number of walks to take each time a new point was proposed. In the bilby implementation, we still take an argument walks which has the new meaning: the minimum number of walks to take (this ensures backward compatibility). Meanwhile, we add two new arguments

  1. maxmcmc: the maximum number of walks to use. This naming is chosen for consistency with other codes. Default is 5000. If this limit is reached, a warning will be printed during sampling.

  2. nact: the number of auto-correlation times to use before accepting a point.

In general, poor convergence can be resolved by increasing nact. For GW events, we find a value of 10 is typically okay. You can revert to the original dynesty implementation by specifying sample="rwalk_dynesty".

Understanding the output

Before sampling begins, you will see a message like this

10:42 bilby INFO    : Single likelihood evaluation took 2.977e-03 s
10:42 bilby INFO    : Using sampler Dynesty with kwargs {'bound': 'multi', 'sample': 'rwalk', 'verbose': True, 'periodic': None, 'reflective': None, 'check_point_delta_t': 600, 'nlive': 1000, 'first_update': {'min_eff': 20}, 'walks': 10, 'npdim': None, 'rstate': None, 'queue_size': None, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'enlarge': 1.25, 'bootstrap': None, 'vol_dec': 0.5, 'vol_check': 2.0, 'facc': 0.2, 'slices': 5, 'update_interval': 600, 'print_func': <bound method Dynesty._print_func of <bilby.core.sampler.dynesty.Dynesty object at 0x7f29e1c47e10>>, 'dlogz': 0.1, 'maxiter': None, 'maxcall': None, 'logl_max': inf, 'add_live': True, 'print_progress': True, 'save_bounds': False, 'n_effective': None, 'maxmcmc': 2000, 'nact': 10, 'jacobian': <function jacobian at 0x7f29ba0411e0>}
10:42 bilby INFO    : Checkpoint every n_check_point = 200000
10:42 bilby INFO    : Using dynesty version 1.0.0
10:42 bilby INFO    : Using the bilby-implemented rwalk sample method with ACT estimated walks

This tells you that a typical likelihood evaluation takes a few milliseconds. You can use this to gauge how long the run might take: if a typical likelihood evaluation takes more than a fraction of a second, it is unlikely your run will complete in a reasonable amount of time using serial bilby. After this, is a list of all the kwargs passed in to dynesty. Note, where a None is given, dynesty will fill in its own defaults. Then, we get a message about how often checkpointing will be done, the version of dynesty, and which sample method will be used.

During the sampling, dynesty writes an update of its progress to the terminal (specifally, this is writtent to STDOUT). Here is an example:

1015it [00:08, 138.49it/s, bound:0 nc:2 ncall:2714 eff:37.4% logz-ratio=-67.89+/-0.09 dlogz:181.166>0.10]

From left to right, this gives the number of iterations, the sampling time, the iterations per second, the bound (while bound=0 dynesty samples from the unit cube), the number of likelihood calls per sample nc, the total number of likelihood calls ncall, the sampling efficiency, the current estimate of the logz-ratio (monotonically increases) and the estimated remaining log evidence.

If the likelihood calculates the log_noise_evidence, then this output will give the logz-ratio. If it doesn’t it instead uses just the unnormalised evidence logz.

The logz-ratio and dlogz gives an estimate of the final expected evidence. You can compare this to your expectations to help diagnose problems before completing a run. However, be aware the dlogz does not monotonically decrease: if a region of large likelihood is subsequently found, the dlogz will increase.