parameters

• main_baygaud = <path>

Path to where script 'baygaud' is.

<baygaud>/src/bin/baygaud by default

• ncolm_per_core = <integer>

Horizontal scale of the segment in pixel unit

• nsegments_nax2 = <integer>

Number of division in vertical direction for segments

• n_cores_total = <integer>

Number of cores to use

• sleep_time = <float>

Sleep time between segments run

• bayes_factor_limit = <float>

bayes factor limit

# multinest parameters (For details, see Feroz et al. (2008) and/or https://github.com/JohannesBuchner/MultiNest https://johannesbuchner.github.io/PyMultiNest/pymultinest_run.html)

• ins = 1 or 0 (?)

importance nested sampling (INS); 1 and 0 means 'True' and 'False' respectively.

• const_eff_mode = 1 or 0 (?)

run in constant efficiency mode; 1 and 0 means 'True' and 'False' respectively. (slower, but more accurate evidence estimation)

• nlive = <integer>

number of (live) points used in every iteration

• mmodal = 1 or 0 (?)

mode separation; 1 and 0 means 'True' and 'False' respectively.

• efr = <float>

defines the sampling target efficiency; 0.8 and 0.3 are recommended for parameter estimation & evidence evaluation respectively.

• tol = <float>

evidence tolerance factor; when this tolerance condition on the evidence defined by tol is fulfilled, whatever happens first.

• feedback = 1 or 0 (?)

need update on sampling progress?; 1 and 0 means 'True' and 'False' respectively.

• write_multinest_outputfile = 1 or 0 (?)

write output files?; 1 and 0 means 'True' and 'False' respectively.

• max_iter = `

maximum number of iterations; a non-positive value means 'infinity'

# TIME LIMIT FOR EACH MULTINEST RUN

• acceptance_rate_limit = <float, 0 to 1>

# profile s/n limit

• profile_sn_limit = <float>

Signal to noise ratio threshold

• acceptance_rate_grad_limit = <float, 0 to 1>