BayesFusion Support Forum

Clearly, your influence diagram is too complex. Please keep in mind that probabilistic inference is worst case NP-hard and it is not that difficult to construct models that are not computable. Quite likely if a model cannot be solved by SMILE/GeNIe, you cannot solve it with any software on Earth ...

Your model is quite impressive (in terms of size and complexity). I am not sure you can solve it using our default ID algorithm, as it will involve calculating the expected utilities over 2^19 (524,288) different policies. This is, of course, when you change back the RCM* nodes to decision nodes ...

I see. I misunderstood your example but have have clarified it sufficiently. When A and B have an unobserved common parent C, you can write the following:

P(A,B)=Sigma_over_i P(A|B,C_i)P(B|C_i)P(C_i)

You write this formula for all possible states of C_i. Because both A and B are dependent on C ...

Please look at a good Bayesian networks textbook. Pearl 1988 perhaps?

When two adjacent variables are dependent, you can see it in the CPT: the distributions are different for different states of the parent. P(A,B)=P(A|B)P(B) for A and B being dependent. When they are independent, P(A|B)=P(A), i.e ...

Don't the formulas in my earlier post help?

P(E)=P(E|AB)P(AB)+P(E|A~B)P(A~B)+P(E|~AB)P(~AB)+P(E|~A~B)P(~A~B)
vs.
P(E)=P(E|AB)P(A)P(B)+P(E|A~B)P(A)P(~B)+P(E|~AB)P(~A)P(B)+P(E|~A~B)P(~A)P(~B)

You can construct a simple model in GeNIe and plug in some numbers in the CPTs to see whether the ...

What exactly do you mean by a fuzzy Bayesian network? There is a good chance that GeNIe does not implement it. We implement standard Bayesian networks, hybrid Bayesian networks (a mixture of discrete and continuous variables, very close to systems of simultaneous structural equations), dynamic ...

What exactly do you mean by a Fuzzy RB?
Cheers,

Marek

The meaning of the diagnostic values is purely theoretical and there are no units. They are essentially cross-entropies between a node and the fault in question. When it is 0.006, I agree that it is rather small. The interpretation is that when you learn the value of this node, you will not learn ...

Please excuse me for a delay in answering -- I was traveling and had a lot to catch up after the travel.

Thank you for the clear explanation of the problem. Now I see what you are after. I think the attached model will do the trick. You will need to enter evidence for the nodes Observation? (either ...

Hi Bahman,

I have looked at your network and it is not really Node13 that is the culprit. Please note that your network will not update even if you remove this node. Generally, it is not that easy to predict the size of the junction tree in advance without trying triangulation. Your network is ...

I’m on the road, so I will be very brief. A small percentage of invalid samples is generally not a problem. Gray arcs tell you that there is something wrong with the definition of the child. They mean, essentially, that they are not necessary (the child is independent of the parent), which means ...

Thank you for the network. I can see that the domain of the node Blood_Sugar is [0.4,4.4] and the definition is Blood_Sugar=Weibull(451.545,0.365594). The samples from your Weibull span between practically zero and almost 400K. Many, many samples fall outside of the interval [0.4,4.4] and this is ...

Hi Nira,

It is hard to say without looking at the node in question and its parents and their definitions (mainly domains). Essentially, GeNIe complains here that there have not been enough samples to derive a precise CPT for the node in question. There may be many reasons for that, so suggesting ...

This is a "million dollars question" :-). Populating CPTs (Conditional Probability Tables) is generally a laborious task. If you have data that you can learn from, it helps a lot -- the tables can be learned from data. If not, then manual labor, getting the numbers from expert knowledge. One thing ...

Your screen shot shows that the definition of the node Munkaterulet is trivial, i.e., none of the parents influences/makes any difference to the posterior marginal probability of the node. You can also see it in the color of the incoming arcs, which are all grey. This means that the node ...