## How can I add a question here?¶

See Contributing for how to report bugs and ask questions. (hint: open a github issue)

## How do I suppress the output?¶

To suppress the logging to stdout, you can configure your own logger:

import logging
logger = logging.getLogger("ultranest")
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.WARNING)
formatter = logging.Formatter('[{}] [%(levelname)s] %(message)s'.format(module_name))
handler.setFormatter(formatter)


You may want to alter the above to log to a file only. See the logging python module docs.

To suppress the live point visualisations, set viz_callback=False in sampler.run().

To suppress the status line, set show_status=False in sampler.run().

## How should I choose the number of live points?¶

Try with 400 first.

In principle, choosing a very low number allows nested sampling to make very few iterations and go to the peak quickly. However, the space will be poorly sampled, giving a large region and thus low efficiency, and potentially not seeing interesting modes. Therefore, a value above 100 is typically useful.

## How can I use fast-slow parameters?¶

If you want some parameters to be updated less frequently than others in the search for a new point, use the ultranest.stepsampler.SpeedVariableRegionSliceSampler. The high-dimensional tutorials shows how to use a step sampler.

## How can I speed up my problem?¶

Computationally:

Algorithimically:

• Use a step sampler (see the high-d tutorial example <example-sine-highd.html>)

• Try to alter your model

## How can I verify that the step sampler is correct?¶

• Increase (double) the number of steps. If the logz remains the same, chances are good that the result is reliable.

## What does the status line mean?¶

You may see lines like this:

Z=0.3(43.44%) | Like=89.39..96.29 [89.3916..89.3936]*| it/evals=39671/11660719 eff=0.2939% N=400


Here is what the parts mean:

                                                                         total number       number
current   of likelihood      of live
iteration  evaluations        points
|       |                   |
Z=0.3(43.44%) | Like=89.39..96.29 [89.3916..89.3936]*| it/evals=39671/11660719 eff=0.2939% N=400
|    |           ------+-----   -------+--------                                 |
|    |                 |               |                                    current effiency
|    |                 |               +- likelihood range targeted by strategy
|    |                 +- lowest and highest likelihood
|    |                    of current live points
|    |
|    +- Progress. Completed fraction of the integral.
|                 (related to frac_remain)
|
+- current logz estimate


## What does the live point display mean?¶

You may see displays like this:

Mono-modal Volume: ~exp(-5.89) * Expected Volume: exp(-2.02)

param1:      +0.0|         *********************************         |     +1.0
param2:      +0.0|         *********************************         |     +1.0
...


They are very useful if you understand them. Here is what the parts mean:

   how many         how large the                  ow large the
clusters        volume should be               MLFriends region
|            based on iteration              is (not subtracting overlaps)
|                   |                             |
Mono-modal Volume: ~exp(-5.89) * Expected Volume: exp(-2.02)

For each parameter you will find a simple linear plot of the live points:

param1:      +0.0|         *********************************         |     +1.0
|            |                   where live points are                     |
|          lower value                                               upper value
parameter name

Live points are shown as *, or numbers, which indicate which cluster they
belong to. Sometimes too many clusters are being found, but that does
not make the result incorrect. Increasing the number of live points
can avoid this (use >100).


## How should I cite UltraNest?¶

The main method (MLFriends) is described in:

• Buchner, J. (2014): A statistical test for Nested Sampling algorithms

• Buchner, J. (2019): Collaborative Nested Sampling: Big Data versus Complex Physical Models

So it is appropriate to write something like

We derive posterior probability distributions and the Bayesian
evidence with the nested sampling Monte Carlo algorithm
MLFriends (Buchner, 2014; 2019) using the
UltraNest[https://johannesbuchner.github.io/UltraNest/] software.


If you use the corner plot, also cite corner. If you use the trace or run plot, also cite dynesty.