BayesFusion Support Forum

I confirm that SMILE does not support parameter learning in hybrid networks.

Correct, the .so file from the .tar.gz archive is for the scenario where you do not use pip.

I have sent you a download link using private forum message.

Later we call the EM algorithm for parameter learning on this constructed network `cate_net`, the initial numbers like 0.5, 0.5, 0, 0 etc does not matter right?

If you have the complete dataset (no missing entries), the choice of initial parameters does not matter, unless you set the equivalent ...

If you are attempting to learn parameters using a dataset without missing data, the selection of initial parameters (uniformize vs randomize vs keep original) does not matter - the node probabilities are calculated by counting records in the data. Just instantiate the EM object and call em.Learn ...

Which Linux distro runs on your cluster?

I have sent you a download link; please check your private forum messages.

I tried to learn parameter for the predefined network structure. Why is the parameter learned wrong?

You have found a bug in SMILE. Depending on the order of node creation, the equations created by the continuous parameter learning may be incorrect. If you modify your program to create nodes in ...

Your code is correct.

One possible issue is your choice of options for EM. When the dataset is complete (no missing data elements), the parameter learning uses case counting and obtains the CPT parameters after one pass over the data. When the dataset has missing entries, we generally recommend ...

But, how to understand this statement "The order of the coordinates reflects the order in which the arcs to the node were created. The most significant (leftmost) coordinate will represent the state of the first parent. The state of the node itself corresponds to the least significant (rightmost ...

A = create_cpt_node(cate_net, "A", "A", ['a1', 'a2', 'a3'], 10, 20)

The create_cpt_node function defined in the tutorial first creates the chance node, which by default has two outcomes and its CPT is { 0.5, 0.5 }. If there are more than two outcome ids, the Network.add_outcome is called to add ...

We were not able to reproduce your scenario exactly, but I think we know the root cause of the issue.

GeNIe (or, more precisely, SMILE) caches the discretized CPTs for equations. These cached CPTs are not invalidated when zero avoidance flag changes. Therefore, if you update beliefs and ...

One more thing: the zero avoidance will be actually used only if the node equation contains probability distributions. What is the equation of the "Sospetto romp..." node?

We have added a network property to control the sizes of axis labels in node barchart. The next GeNIe release will go public next week, and you will be able to specify the point size of the font.

Regarding the screenshot use in the presentation - you can use the Copy command in GeNIe, then paste ...

Your second screenshot shows the CPT with zero avoidance - there are no zeroes.