BayesFusion Support Forum

The error is caused by C++ exception caught by code binding between core SMILE library and PySMILE. It's a memory issue. Are you running 32-bit or 64-bit Python?

I use the Learn parametres function

The 'Learn parameters' command only learns parameters, and works on the model structure already present. Are you referring to the output from the PC structure learning algorithm?

The min/max/avg are calculated over sensitivities of all parameters in node's CPT. The sensitivity algorithm calculates a separate sensitivity value for each of the CPT entry for each target state of each target node. You can view per-parameter sensitivities in the node properties/definition when se...

One more thing: if your calculate_stress function returns a list of values sampled from unknown distribution, then you can try to fit your samples to a metalog distribution. GeNIe/SMILE support Metalog since GeNIe version 4.0. To fit the data, use GeNIe's Tools|Metalog Builder window, or visit an on...

You can set node equation (as a string) with Network.set_node_equation. The actual formula depends on the type of the output from calculate_stress. To experiment with various node distributions using SMILE notation, you can use GeNIe's probability visualizer (available from the Tools menu), or go to...

Re: item 3, we will add an option to avoid zeros in the learned parameters. We already have similar functionality in the hybrid inference sampling algorithm. The replacement value for zero will be 1/N, where N is the numbers of records in the dataset.

Thanks for the long and detailed post. We will try to provide answers to all your questions in multiple posts. 1. We have added an entry in our issue tracker re: missing maxSearchTime for the PC learning. This is a simple change, and we should be able to implement it before the upcoming SMILE releas...

It seems the issue is with PC learning algorithm and the missing value replacement which is numeric. I ran PC with a data file with missing values replaced with the label "x99", and the output did contain the x99 outcome. When the missing value was replaced with a number (like 99), the out...

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 attached...

The values displayed in the tornado are actual target posterior changes for the specified parameter spread (see the slider control in the bottom part of the tornado dialog). These are not the derivatives. The coloring of the nodes when the sensitivity analysis is active is based on the derivatives. ...

Please make sure you have the "Notify me when a reply is posted" option checked when creating a new post. To enable notifications in the existing topic/thread, click on the topic tools button (the wrench icon), and select 'Subscribe topic'. I have checked the reply notifications using two ...

Your HOMA_IR node has finite bounds specified (0 to 20). The node equation was used 60000 times during discretization sampling. The equation inputs were uniformly selected from parent intervals (if there were any parents). Out of 60000 values sampled, only 20043 were within the bounds. 25758 samples...

Just sent you a PM with links to GeNIe Academic 4.0.3225. Let me know if copy/paste works for you. What we've found when trying to reproduce the problem was thatr text placed in the clipboard by Excel, then copied to GeNIe running on Wine contain lines ending in the CR/CR/LF sequence instead of just...

We have fixed this issue. New GeNIe release should be available within a week or two. If you want to access a private build, let me know which GeNIe edition you're using (academic vs business).

Recent GeNIe releases have support for diagnosis in hybrid models (running on internally discretised version of the model).

We also have internal builds which use measures other than entropy to calculate the diagnostic values of observations.