BayesFusion Support Forum

At this point, DSL_dataGenerator is not available through wrappers.

I'm sorry; I have misread your original question. The evidence is always passed per slice for the plate nodes in dynamic Bayesian networks. If you want to specify values for all slices, you have to write your own function.

This is correct, the temporal evidence API call takes the slice numbef and the outcome of the plate node for that slice.

In the spreadsheet below, column "A" has values copied directly from GeNIe. Column "I" has values generated using the following Sheets formula:
= Max(1,Min(13,-6+12*rand()+1+12*rand()))

When the number of histogram buckets is reduced, both show similar asymmetry:

skewed histograms.png

I think this is mostly the visualization issue. I will look into discretization, for now please note how Wolfram produces asymmetric histogram too.
wolfram-assymetric.png

You can check your expression with our online probability distribution visualizer at https://prob.bayesfusion.com/- it ...

Here's the quote from GeNIe's manual:

The final tab, Calibration, shows a very important measure of performance of a probabilistic model, notably the calibration curve. Because the output of a probabilistic model is a probability and this probability is useful in decision making, ideally we would ...

You have two options:

1. use an equation node and specify the bounds.
2. use a discrete node with numeric intervals over node outcomes.

For more information, refer to GeNIe manual. It is installed with the program; alternatively you can access HTML and PDF versions at:
https://support.bayesfusion ...

If the dataset used for parameter learning is complete, the EM uses one pass case counting. If equivalent sample size is zero (so initial network parameters have no influence on the output), and there are some CPT entries which have no records in the dataset, there will be 1.0/nodeOutcomeCount added ...

Support for continuous/hybrid dynamic Bayesian networks was added to PySMILE. It will be released as part of the next SMILE update. Private build is available for those users who need to work with equation nodes in DBNs.

I don't know whether this is a intrinsic computation constrain with GeNIe or my laptop's limitation (I7, 32G).

If you can share your model (and the evidence set, preferably as a case(s) defined in .xdsl) we'll be able to check the triangulation statistics using our internal tools.

QGeNIe internally builds the CPTs for each node based on the incoming arc types/strengths. You can check it using the "Export to GeNIe" command available in the File menu.

As you pointed out, the algorithm used to populate the CPT is not directly applicable to the scenario where node and/or its ...

The network can be densely connected and triangulation can fail to produce a reasonably sized jointree, even with 38 nodes.

Can you share your model, so we can investigate? You can send me a forum private message if you don't want the network to be publicly available.

Please note that cooer representing the sensitivity of the node in GeNIe's graphical view is based on the max sensitivity over all node's CPT entries. I'd go for the max as the quantitive value. A node does not have a single derivative, there's one for each probability in node's definition.

I also found that by default `model.get_rand_seed()` is 0. When it is zero, the inference result will change each time I run.
After using `model.set_rand_seed(seed)` to set a non-zero seed, the result for each run is consistent.

That is correct. Using the seed equal to zero will initialize the ...

(1) Why even though I set a specific seed, each time I print the posterior of target variable, it still has variability?

This should not happen, the pseudorandom generator is seeded from the value specified in network properties. If you can share your model, we can investigate this.

(2) During ...