BayesFusion Support Forum

Thank you for your kind words. We hear a lot of good things about GeNIe and SMILE from their users. They are in no way inferior in their functionality, speed, and reliability to the "crazily expensive" software and are only getting better with time.
Cheers,

Marek

I'd like to confirm that you cannot pass posterior/marginal probability distributions as arguments to other nodes in a model. The main reason for this design decision is that we make a clear distinction between definitions/domains and values. Inference algorithms calculate values (including ...

I may be understanding your description incorrectly (I had a hard time connecting the models to your description, as you are using different symbols -- it may be obvious for you but it is not for me :-)), but is your question related to being able to use probability distributions as arguments to ...

I'm afraid we don't use any smoothing, at least consciously :-). Can you tell use more about it?
Cheers,

Marek

You can operate on values but not on their statistical properties (i.e., results) in your equations. Please keep in mind that a Bayesian network is equivalent to a system of simultaneous equations. Your new variable could have a conditional statement that compares the number of positive cases to ...

I hope I understand your query correctly, as it contains several terms that could mean various things (like "bottom event", "base event", "upper and lower values", "failure probability", "fuzzy function"). It seems to me that what you want to do cannot be done in GeNIe but you can do almost anything ...

Most certainly. GeNIe and SMILE include several Monte Carlo sampling algorithms and produce samples for each of the nodes in the model, whether the model is discrete, continuous or hybrid. Please look at the section on algorithms for Bayesian networks.
Cheers,

Marek

By changing the definition of the child node. The conditional probability distributions describe precisely the impact that the parents have on it. In your model, the conditional probability distributions (columns in your CPT) are all equal.

I hope this helps,

Marek

The guilty element of your model is the CPT in n10:

s0 0.5 0.5 0.5 0.5
s1 0.5 0.5 0.5 0.5

Please note that the probability distribution over n10 is not impacted by the parents.
Does this help?

Marek

Thank you for sending me your model. I have looked at it and saw that quite likely you mean the nodes n0, n6, n3, n4, n12 , n11 and n9 in your obfuscated model. Each of these nodes is defined by a CPT that consist of only zeros and ones. Zero and one are special numbers in probability theory. Once a ...

Both parameters are accessible from the Learn New Network dialog. The upper right pop-up menu allows you for choosing the algorithm. Have you actually looked at GeNIe manual :-)? You can send me the data set by a private message on the Forum or by Email (marek@bayesfusion.com).
Cheers,

Marek

Hi Ellen,

If the algorithm that you are using is the PC algorithm, it is possible that the structure that it discovers is too complex. There may be nodes that have many parents. In that case, the crash occurs when you go to the next step, i.e., learn parameters. The probability tables are huge and ...

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 ...

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 ...

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 you call ...