BayesFusion Support Forum

It look ok, although many entries in the CPTs do not seem to be updated because of a lack of data.

There is a big difference between learning with and without unrolling. If you unroll, the CPTs at each time step are learned separately. However, these CPTs are usually assumed to be identical and ...

The right way is to perform learning on the original network and never on the unrolled network. This way your predictions should be much more accurate. Can you give this a try?

Hi Mark,

Thanks for the reply. Yes, I tried it like that as mentioned in my previous post. Did you manage to take a ...

A few remarks:

It seems to me that the amount of data is insufficient to learn the number of parameters you have in the model. Is it possible to obtain more data?

Did you include the learned network in the zip file? All the probabilities in the CPTs in DBN_mark_test_17411.xdsl are uniform so I ...

Can you also post the data file? And the learned network?

Hi Mark,

I have attached the DBN and data file. The data is for 4 time slices. I would like to predict all the variables at 5th time slice. As we can see in the unrolled and learnt network, the probabilities are not changing at all and ...

This is all possible in GeNIe. CPTs are accessible by double clicking on the nodes and predictions can be made by instantiating evidence and performing inference. This is a manual process, but it can be automated in SMILE.

Hi Mark,

Based on the DBN model attached in my previous post, can you ...

I don't understand what you are trying to do. Could you describe it in more detail, please?

Hi Mark,

Thanks for the reply. I want to perform prediction for several time steps based on the learned parameters and transition probabilities. For example predicting states of several parameters say ...

Hi all,

Please help. After I learnt the network parameters and transition probabilities, I aim to perform three-step prediction of several parameters. Is it possible in GeNIe? How can I see the new transition probability matrix that is learnt?

Cheers,
\KSM

anyone?

Regards,
\ksm

Thanks for the reply Mark!

I tried learning my DBN in GeNIe and it works OK. However, I notice only slight changes in the transition probabilities due to which If I do one step prediction of all the variables, I am not able to get correct answers. When running EM algorithm, I selected randomize ...

Anyone? Some pointers will be appreciated.

Thanks,
/ksm

HI all,

I am interested in performing learning and inference in DBN using the EM algorithm. I have a network with 5 slices with two hidden nodes (children) and four observation nodes (parents to each child).I am having difficulties in understanding how to learn transition probabilities . I know ...