| Re: [recommenders-dev] Jayes - Likelihood Evidence |
Thanks a lot!
Greetings,
christian
-----Ursprüngliche Nachricht-----
Von: recommenders-dev-bounces@xxxxxxxxxxx im Auftrag von Michael Kutschke
Gesendet: Fr 25.07.2014 07:45
An: Recommenders developer discussions
Betreff: Re: [recommenders-dev] Jayes - Likelihood Evidence
Hi Christian,
I found the information i needed. Looks more complicated than i initially
thought, but less complicated than i thought yesterday. I will push a
change tomorrow.
Regards, Michael
Am 24.07.2014 15:17 schrieb "Christian Lemke" <lemke@xxxxxxxxxxxx>:
> Hi Michael,
>
> given the distinction between soft and virtual evidence, a handling of
> soft evidence is what is needed for my use case, because the given
> information/evidence the system recives (from an external source sharing a
> subset of random variables of the BN in use) has actually to deal with
> concrete probability distributions which should be treated the same way as
> hard evidence would be. In this sense (proper) hard evidence can be
> interpreted as a special case of soft evidence represented by probability
> distributions containing only zero and one values.
>
> Greetings,
> christian
>
>
> Am 24.07.14 09:18 schrieb "Michael Kutschke" unter <
> kutschke.michael@xxxxxxxxxxxxxx>:
>
> Hi,
>
> what makes a difference is this: Are we talking about soft evidence, which
> means that you observe an actual distribution, or virtual evidence, where
> you have a distribution P (x is observed| x is the actual state). I assume
> most people only need the latter, and that can be directly encoded in your
> model, no changes to the algorithm required. You would just need to add an
> extra node for the observation. The point here being that the real value
> is also modeled as a hidden variable, distinct from the observation itself.
>
> Regards, Michael
> Am 24.07.2014 09:01 schrieb "Michael Kutschke" <
> kutschke.michael@xxxxxxxxxxxxxx>:
>
>
> Hi Christian,
>
> i would look into JuntionTreeAlgorithm, inside the method updateBeliefs,
> there should somewhere be a call to the method that sets the evidence. That
> could be a starting point. Until i understand the reqirement better and
> read up a bit on soft evidence, i have to admit though that anything i say
> about what it takes to make it work like you expect is entirely guts
> feeling.
>
> Regards, Michael
> Hi Michael, thanks for your immediate response to my question!
>
> I consider to use Jayes as an inference component in a system which
> recives evident information with certain degrees of uncertainty. So it's
> possible that the requirements of my intended use case are different from
> those Jayes is designed for.
>
> What are the crutial parts in the code to be changed, to enablbe a proper
> handling of soft evidence in the Inferer classes?
>
> Greetings,
> christian
>
>
> -----Ursprüngliche Nachricht-----
> Von: recommenders-dev-bounces@xxxxxxxxxxx im Auftrag von Michael Kutschke
> Gesendet: Mi 23.07.2014 15:06
> An: Recommenders developer discussions
> Betreff: Re: [recommenders-dev] Jayes - Likelihood Evidence
>
> Hi Christian,
>
> Only hard evidence is supported. You could simulate soft evidence by
> changing the probability distribution of your variable, but that would be a
> lot less efficient because the internal structures would have to be
> rebuilt. Supporting the use case in an efficient manner would not be too
> hard to implement though, i think. It just has not appeared as requirement
> so far. What is your goal?
>
> Regards, Michael
>
>
> Am 23.07.2014 14:57 schrieb "Christian Lemke" <lemke@xxxxxxxxxxxx>:
>
>
> Hello,
>
> I have a question about the evidence concepts supported by the
> Jayes implementation of a Bayesian network.
> Does Jayes offer the possibility to set an arbitrary (but valid)
> probability distribution (e.g. [0.75,0.25]) as likelihood evidence for a
> node in the BN or is only the usage of hard evidence ([1.0,0.0]) supported?
>
> Thanks,
> christian
>
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