BayesFusion Support Forum

Hi,
i was wondering how can i obtain the actual mathematical equation for obtaining probability of P(C/xi,yi,ti,xf,yf,tf), based on the attached network structure.
i really appreciate your help.

Kind regards
Sam

[quote="mark"]In SMILE you can use network.FindNode("node-id") to find any node in the network, including leaf nodes. Also, are you sure you want to use theMatrix->Subscript() instead of node->Value()->SetEvidence()?[/quote]

well by setting evidence nothing will change on utility node and evidence ...

Hya,
is there any way to instert and learn hidden node parameter in this software ??
thanks

hi is there anyway to learn the network online, so if u train the model off line initially then when u determin some reading is wrong or has been missclassified then relearn the netwrok. in other word performing sequential updating of BN without having the data which were used to learn the initial ...

i have attached the genei version as an example, so in here i have 2 nodes and one value node which i can measure the expected value if i feed in the "dis" and "DNo" in geni u just locate which node u r measurments fit in then set the value to one but in smile i compute which interval my each ...

the question i have is when u create influence diagram, u need to input the measurements which are discritisized, in genie it is easy u just find which leaf node it is located and set that value to 1, but in smile u need to calculate which leaf node to trigger. is there any easy way of locating leaf ...

Hi thanks for you answer but my real intention by this example is to understand how I can train the continuous parameter and evaluate it by value node (so if I have r g then how can i get posterior without discreting the parameters).when I try to connect the arc between value node and continuous ...

hi i am trying to use smile to get the ExpectedUtility or posteriori by feding some values, i have discretized the parameters in training ... i have manged to set leaf note parameter to 1 here is simple example
DSL_Dmatrix *theMatrix;
theNet.GetNode(HRI)->Definition()->GetDefinition(&theMatrix ...

well i have tried that already and it does not work i get the same error that i can not use the continuous and discrete parameter together, however i have managed to get rid of this error by changing one of the skin value to 0.1 instead of 0, then even if i get that how do i make inference to ...

Hi Mark thanks for quick response
the reason skin is 1 cos I inputted grand truth data(positive samples) where i am confident that r g gives skin colour i.e. I set them all to 1, when I try to train this it gives error where I cannot train discrete parameter with continuous one so i came up with ...

hi i want to classify skin pixel based on thier intesity in normalize rgb space i use only 2 channel for r and g as attached i was wondering if i train this by PC method how can i use it to make a decision(or get posteriori) if the pixel is skin given the r g ??
thanks