BayesFusion Support Forum

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...

Hi Fabrice, What you want to do mixes values (high, moderate, and low) with uncertainty about them (probability of "low" > 0.7). Bayesian networks are models consisting of variables, each of which has its domain (these are values), and interactions among them, which can be best thought of ...

Hi Yan, Yes, you are correct in that if you want to see the effect of both sport and drinking water, you should control both. In the example that you gave, the effect on health of controlling both will be the same as the effect of observing both. In fact, you can see the effect in the CPT for node C...

Hi Yan, Let me try to answer your questions. 1. My understanding is that entering evidence can only give 100% for one state of node A, while virtual evidence can give the probability distribution (e.g., 20%, 40%, 40%, assuming there are three states) for all states of node A. Is it right? If so, doe...

Hi Camilla, I'd love to look at this model to make sure that I am understanding your question correctly. Perhaps we can meet online? Please shoot me a private message so that we can set the time. Alternatively, perhaps you can share with me the relevant part of the model? You can use GeNIe obfuscati...

You can't do it in GeNIe. What you can do is transform your data in Excel by creating for a variable x a new variable/column named, e.g., log_x) and then save the file as .CSV and load it into GeNIe. You can use log_x instead of x in learning.
I hope this helps,

Marek

Hi Yan, I have looked at your data and have managed to replicate the problem. Clearly, there is a bug in PC and, as you wrote, the algorithm ignores the max. adjacency size parameter. We will work on it and will fix it in the next release. When I face a problem like yours, I usually play with the al...

Hi Yan, Let me try to answer your questions. (1) This is hard to say, as the two algorithms work on different principles. I would try both and look for commonalities in the output. This especially that your data set is rather small. Please play with the significance level alpha in PC -- it has a big...