Hello Marco,
> By any chance, are using that for prediction?
Yes, I am using it for prediction.
> To compute P(A = a | whatever you conditioned on), just sum the
> corresponding weights over the total weight mass. Since no language
> trickery is involved, this works reliably.
Thank you! It's exactly what I needed.
---
BR,
Alexandr
On 11 November 2014 23:33, Marco Scutari <marco.scut...@gmail.com> wrote:
> Hi Alexandr,
>
> On 11 November 2014 00:10, Alexandr M <rus...@gmail.com> wrote:
> > Sorry that I formulated my question not very accurately.
> > I form expressions/(logic conditions for parameters evidence and event)
> > dynamically inside the loop and they are sometimes quite long.
>
> By any chance, are using that for prediction? Because predict(...,
> method = "bayes-lw") does posterior predictions from any set of
> variables.
>
> As an alternative, you can do cpdist(..., method = "lw") which also
> generates from the posterior distribution:
>
> > str(cpdist(fit, node = "A", evidence = list(B = "b"), method = "lw"))
> Classes ‘bn.cpdist’ and 'data.frame': 10000 obs. of 1 variable:
> $ A: Factor w/ 3 levels "a","b","c": 1 2 2 3 1 1 1 2 1 1 ...
> - attr(*, "weights")= num 0.114 1 1 0.428 0.114 ...
> - attr(*, "method")= chr "lw"
>
> To compute P(A = a | whatever you conditioned on), just sum the
> corresponding weights over the total weight mass. Since no language
> trickery is involved, this works reliably.
>
> Cheers,
> Marco
>
> --
> Marco Scutari, Ph.D.
> Lecturer in Statistics, Department of Statistics
> University of Oxford, United Kingdom
>
--
Best regards,
Alexander Maslov
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