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Running Inference (after adding findings) without re-compiling the network all over again

Unanswered Running Inference (after adding findings) without re-compiling the network all over again

  • Wednesday, 28 March 2012 4:23 PM
     
     
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    Hi,

    I'm currently experimenting a bit with Infer.Net, trying to use it for Bayesian networks (very much in the same way as the 'Sprinkler, Cloudy, Rain'-example that can be found in the tutorials).

    I have a question regarding when the network decides to compile the Model. I assume that the ideal way is to: build a network, compile it once, and then run inference over it numerous times.

    When I look at the messages (Engine.ShowProgress) I see that after I add a new ObservedValue to one of my Variables, and perform Inference, Infer.Net re-compiles the model (before running Inference).

    When you look at the example that can be found in the tutorials ('Sprinkler, Cloudy, Rain'-example), and turn on 'ShowProgress, Infer.Net re-compiles the model for every new query of Inference.

    For example the output of the first two queries, is like this:

    

    Am I wrong in thinking that compiling the model should only happen once?

    Thank you in advance for your help.

    With regard,

    Thomas

     

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  • Wednesday, 28 March 2012 4:36 PM
     
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    Maybe a better question should be: Can I determine the posteriors of the network after adding Observed Values.

    I understand that I would have to run Inference, but don't really agree on re-compiling the whole network again.

    I've tried to do this in the following ways (both re compile the model):

    // Adding observed values:
...
myVariable1.ObservedValue = myObservedValue1;
myVariable2.ObservedValue = myObservedValue2;
...

// Running Inference - method 1:
var myPosterior = Engine.Infer<Discrete[]>(myVariable1);
Vector myProbs = myPosterior[0].GetProbs();
...


// Running Inference - method 2:
var ca = Engine.GetCompiledInferenceAlgorithm(myVariable1);
var myPosterior = ca.Marginal<Discrete[]>(myVariable1.NameInGeneratedCode);
    Vector myProbs = myPosterior[0].GetProbs();

    Thanks again.

    With regards,

    Thomas


    • Edited by ThoFlam Wednesday, 28 March 2012 4:38 PM
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  • Friday, 30 March 2012 5:37 PM
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    A model is recompiled when the pattern of observations change, not when the observations themselves change. If you observe something one time, and not the next time, you are removing a constraint from the model, and the generated inference code to give the best approximation to the posterior must change also. To avoid recompilation, you should create a seperate instance of your model class for each pattern of observations. Then there will be one compilation for each pattern, but thereafter no more recompilations. In a typical simple situation, you have a model for which you (a) want to learn the parameters, then (b) fix the parameters and do predictions. In this case, create a model class that defines the model, along with methods to run (a) and (b), but then create two instances.

    John

     
  • Wednesday, 4 April 2012 4:46 PM
     
     
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    Besides John Guiver's solution, another approach is to build the model to handle missing data.  Then you do not have to recompile when the pattern of observations changes.