Fixed the way random walker starts are picked over a boundary #89
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samaloney
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samaloney
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Dec 8, 2022
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I think this approach still assumes that the parameter bounds are close to the best value. For things like emission measure I've often made the bounds very large (like 1e-5 to 1e20). The Why not make |
Co-authored-by: Shane Maloney <shane.maloney@dias.ie>
KriSun95
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…oundary for MCMC analysis.
… hard-coding the wrong one.
Codecov ReportBase: 54.63% // Head: 54.86% // Increases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #89 +/- ##
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+ Coverage 54.63% 54.86% +0.22%
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Files 19 19
Lines 3115 3115
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+ Hits 1702 1709 +7
+ Misses 1413 1406 -7
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settwi
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* Fixed the way random walker starts are picked over a boundary for an MCMC run. * Added test for the method that starts walkers off randomly over the boundary for MCMC analysis. * Now using numpy for the largest 32-bit integer rather than, mistakeny hard-coding the wrong one.
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The way I did this in the first place was stupid. Originally the parameter space bounds were multiplied by 100 and then I choose random integer values (why do I want to make everything an integer!?) within the new scaled bounds. This is obviously not right.
The new way is done as L+(U-L)*R, where L and R are the lower and upper bounds for the parameter space and R is a random value between 0 and 1. This should now give values that make sense no matter the bounds.