BayesFusion Support Forum

A probability distribution that does not add up to one is impossible and wrong. GeNIe will not let you exit the dialog in which you enter such distribution. I don't know for sure, just guessing, but I think that you may want to represent a dummy probability distribution that will never be used in so...

Thank you for the additional description. The lognormalCDF() does make sense (I assume that this is what your theory dictates). I still think that if you want to make the two nodes parents of Node5, you should make sure that the discretization of both makes sense and then specify the CPT of Node5 co...

I will be happy to try to help. I have a hard time understanding the definition of the node "damage state". You are making several calls to the Uniform() function and this confuses me somewhat. Please note that as Uniform() generates a random number from the Uniform distribution, each time...

Absolutely! Please try Layout menu and then Graph Layout. There are two simple algorithms that do a reasonable job in rearranging nodes for readability.
I hope this helps,

Marek

Hi Salim,

The screen shots are not originating from GeNIe and are confusing. My original advice is still standing. You should change the uniform probabilities into the probability distributions that will be there when the node Risks has no effect on the children nodes.
Cheers,

Marek

Hi Salim, I will be happy to help you but need to understand better what you want. Would you be willing to create and post the simplest possible influence diagram that does what you want? In general, uniform probabilities do not sound like a good idea in this case. I would keep the probabilities tha...

Hi Maghnie, The silent crash has to do with the fact that we have not yet programmed this functionality for continuous nodes. We will fix this in one of the next releases. To make it work at the moment, please change the type of the continuous nodes from continuous/equation to discrete chance nodes ...

It seems to me that this is a data set that you can use to train a Bayesian network. An influence diagram would require explicit decisions and also a utility function. Your data set is small (34 records) and you cannot really learn anything useful from data alone. If you create a BN model based on (...

Probability of any evidence in a continuous node will be zero, so you cannot do that even theoretically. I suggest that you discretize your network first and then calculate P(E). You can make the interval around the evidence as small as you want but not empty/zero length.
I hope this helps,

Marek

The Bayesian Search-based algorithms (Bayesian Search and GTT) learn no arcs, PC and Naive Bayes-based algorithms (NB, TAN and ANB) show plenty of dependencies. With 12 data records, whatever you will learn in terms of structure will be completely unreliable. Naive Bayes may do best if one of your v...

Please note that there are many ways in which you can transform the data set to make it suitable for learning. Essentially, the first decision that you need to make is what order your model is, i.e., what is the highest time degree of a link in your model. You will reflect your decision in the struc...

I have read your post but have frankly difficulties in understanding what precisely you want to do from the theoretical perspective. What I understand, perhaps wrongly, is that you would like to observe a node, calculate the impact of that observation, but then clear the observation but preserve its...

In GeNIe, you can see all samples generated on the left hand side in the Value tab. GeNIe displays the number of samples generated for any bar in the histogram when you hover over that bar. Distribution visualizer has the same functionality. We will extend the description in the manual to account fo...

You are proposing an interesting solution that will work well in many case, certainly in this case. Let us look at the simplified model with just three variables. We do get funny (in terms of being large) number of samples in forward inference for small and large values of evidence in v1 (e.g., 0..1...

I have looked at your model and I believe that this is a theoretical problem that is solvable at the modeling stage. Please note that the domain of v2 is {0,10} and when you have a combination of {0,1} at v1 and {-2,-1} at error, you generate a value for v2 that is out of bounds. This is handled eas...