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Can Infer.net support Dynamic Bayes Network and continuous hidden variable in Dynamic Bayes Network ? RRS feed

  • Pergunta

  • Hello .  I'm new to Infer.net .

    I am wondering if Infer.net support Dynamic Bayes Network or continuous hidden variable/ layers in Dynamic Bayes Network ?

    Best

    quarta-feira, 30 de maio de 2012 15:17

Todas as Respostas

  • Hi Heng,

    The answer to your question is yes, and it can be found in the FAQ - Does Infer.NET support Hidden Markov Models (HMMs)? You might want to look at the following examples - click through model and influence model, or search the forum for others.

    -Yordan


    sexta-feira, 1 de junho de 2012 12:45
  • Hi Yordan :

    Thanks for the reply . But what I mentioned "Dynamic Bayes Network with continuous hidden variable/ layers" is more like HMM that to be unroll  ?

    I mean the "with random variable arrays for the variables along the HMM"  , which is not supported in current version ?

    Best


    Heng Lu

    terça-feira, 4 de setembro de 2012 09:53
  • Heng

    Can you be more explicit about your model so that we can focus our advice? In theory you can build pretty much any flavour of HMM via the modeling API - you can have continuous or discrete, array or scalar, and as many layers as you want.

    John

    segunda-feira, 10 de setembro de 2012 14:55
    Proprietário
  • Hi John :

    Yes, my question is like this : Can we build a Dynamic byesian network like this : We have continuous munerical observations  , and  the observation itself is dependent on continuous hidden variables (more than one ).

    For example, we have acoustic features of the speech database as observation , and we assume this continuous acoustic features is dependent on the trajectory of the phone (based ) which is a hidden continuous variable , and on the trajectory of the context information which is also a hidden continuous variable.  What we know is the class of the phone and the context as discrete values as the label of the database. And by the way , the observed acoustic features obey the Gaussian distribution , can be modeled as GMM / HMM .

    Simply , can we model both phone and context info using some distribution , and acoustic feature using GMM /HMM , and the GMM /HMM  is dependent on the trajectory of the phone and context distribution ?

    Best

    Heng Lu


    Heng Lu

    segunda-feira, 10 de setembro de 2012 16:15
  • Hi Heng

    You can certainly define such a model via the Infer.NET API - but it is likely that the inference would not be accurate. The reason is this - if you want the conditional distributions representing the transitions to be Gaussian Mixture Models,  you would really need to use VMP rather than EP, but as VMP does not propagate uncertainty correctly, this is very problematic in long chains. So I cannot recommend doing this. Is there a paper that you are looking at that you are trying to implement?

    John

    terça-feira, 11 de setembro de 2012 13:56
    Proprietário
  • Hi John :

    Thank you . I see what you mean. I am not trying to implement some paper but to try some ideas of my own .

    I have tryed some other toolkit , but most of them do not support continuous hidden variables .

    thank you for the suggestion , I will think about it and try some experiments .

    Best

    Heng Lu


    Heng Lu

    quarta-feira, 12 de setembro de 2012 06:08
  • The link for click through model is not working :(
    quinta-feira, 1 de novembro de 2018 22:17