Dear Ross, On Friday, 3 June 2016, <ross.chap...@ecogeonomix.com <javascript:_e(%7B%7D,'cvml','ross.chap...@ecogeonomix.com');>> wrote: > > I find that repeating the command gives very different results for the > same
set of evidence. Some variability in the results is expected since they are Monte Carlo estimates. What is happening in your case is, I think, that your evidence has a very low probability (since it is so complex) and thus you need to generate more particles to obtain a reasonably precise estimate of that conditional probability. For such a small network cpquery() can easily generate, say, 10^7 particles in a few seconds. Can you please advise me what is happening with these queries and why the > results is so variable and if there are other options for generating > conditional probabilities with bnlearn. > For your query, the default logic sampling is the only option - likelihood weighting does not currently support unbounded intervals in the evidence. Cheers, Marco -- Marco Scutari, Ph.D. Lecturer in Statistics, Department of Statistics University of Oxford, United Kingdom [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.