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rbouckaert committed Mar 20, 2019
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@@ -23,15 +23,15 @@ Say, we have two models, M1 and M2, and estimates of the (log) marginal likeliho
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Note that sometimes a factor 2 is used for multiplying BFs, so when comparing BFs from different publications, be aware which definition that was used.
Note that sometimes a factor 2 is used for multiplying BFs, so when comparing BFs from different publications, be aware which definition was used.


**Nested sampling** is an algorithm that works as follows:

* randomly sample `N` points from the prior
* while not coverged
* pick the point with the lowest likelihood Lmin, and save to log file
* replace the point with a new point randomly sampled from the prior using an MCMC chain of `subChainLength` samples __under the condition that the likelihood is at least Lmin__
* pick the point with the lowest likelihood L<sub>min</sub>, and save to log file
* replace the point with a new point randomly sampled from the prior using an MCMC chain of `subChainLength` samples __under the condition that the likelihood is at least L<sub>min</sub>__

So, the main parameters of the algorithm are the number of particles `N` and the `subChainLength`. `N` can be determined by starting with `N=1` and from the information of that run a target standard deviation can be determined, which gives us a formula to determine `N` (as we will see later in the tutorial). The `subChainLength` determines how independent the replacement point is from the point that was saved, and is the only parameter that needs to be determined by trial and error -- see [FAQ](#nested-sampling-faq) for details.

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