BayesFusion Support Forum

Haha, great, thanks Marek!

Thanks Marek. For the question 3, I'm still a bit confused and would like to discuss with you. For example, node A "sport" (low frequency, medium frequency, high frequency) directs to node B drink water (low frequency, medium frequency, high frequency), and both sport and drink water direc...

Dear staff, I'd like to ask some questions about entering evidence, virtual evidence, and controlling values: 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 ar...

and i try PC algorithm again, the output doesn't show outcomes/states like S_99 (replace missing data with 99), but if i use other structure learning algorithms (e.g., bayesian search), the bar chart view shows S_99, that's a bit weird. could you please check that by randomly using some data? thanks...

The structure learning algorithms in SMILE/GeNIe currently require that a dataset has no missing values. From the POV of the learning algorithm the missing value replacement is no different from any state label. The PC algorithm should output the nodes with outcomes like S_99, just like the attache...

Dear staff, I have some questions about missing values, please see below: (1) if i open the data file and replace all missing values with specified value, will these missing data affect the structure/parameter learning? for example, node A has three values "L, M, H", if i replace the missi...

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 a...

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 bi...

Dear staff, I'd like to ask some questions about structure learning. 1. I have around 130 cases and 7 nodes. In this case, is it better to use "Bayesain Search algorithm" rather than "PC algorithm"? 2. The DAG learned by "Bayesain Search algorithm" is very different fro...

Is there any missing data items in the learning data set? In GeNIe 4.0 when the data is complete, the EM procedure switches to simple counting, regardless of the parameter initialization. There's no missing data. So in this case, which one is reliable? Or I can follow the results of either version?...

Dear staff, I just find the parameter learning of GeNIe 4.0 is a bit different from GeNIe 3.0. Specifically, in 3.0 version, after I select "Randomize" for parameter initialization, the value of Log(p) will change every time I run the model. Normally, I will run the model several times unt...

Hi Yan, I believe you are asking about randomization in the context of the EM algorithm (please correct me if I am wrong). In validation, this parameter also refers to the EM algorithm part (learning the parameters at each cross-validation step). It is an empirical finding that randomization gets y...

Dear staff, Can I ask some questions about "Randomize" option: 1. In the user mannual, it says "Using the Randomize option may be especially useful when learning parameters with latent variables." Could you please explain it in more detail? 2. If we select "radomize" in...

Dear staff,

I would like to know the reason that diagnostic values of obervation ranked nodes are too small (e.g., 0.006) in the diagnosis of GeNIe. Is it common or usual? How can we explain this? Thanks.

Kind regards

Dear staff, Please see the attached example of an BN model. I would like to ask some questions about the tornado diagram. For example, when you set node L as target and run the sensitivity analysis, you will find the most sensitivity parameter for "L = good" is "I = L | D = L" . ...