BayesFusion Support Forum

Thanks very much for your very informative post. I will try learning the structure of my model this time so i can hopefully come up with a better topology and try em with default exact inference algo. Would post back with newer findings in due time.

Thanks once again.

yes boss i did do that though the output did n't turn out to have higher accuracy than the Asis and Epis. I can n't quite remember the exact figure off the top of my head but it was in 40%-ish range. Like i said i have found some issues with my input data where erroneous and missing items in the ...

Hey mate, thanks for your reply, so far from the initial test i have ran i found Asis and Epis give the highest accuracy and precision in my model but that was only close to 58% only. I also found some inconsistencies in my data and i am set out to improve on that , and probably would provide more ...

no i have n't as of yet, but that sounds like a very good idea now though does the default select the best one based on some sort of em generated metrics,if not which algorithm would it be ?

Also out of curiosity i am also interested to find out if there are any plans to implement Gibbs Sampling ...

Thanks so much for that info. I have decided to try out the first option for now. I dumped the two discrete parents with (30 outcomes and 6 outcomes respectively), but i still have like 18-19 parents with 2 outcomes each now the learning is super fast in comparison to before. em tore through 140K ...

I fixed the discrete states setup bug, i now explicitly set
SetNumberOfOutcomes first followed by RenameOutcomes
but i have new problem after setting up all evidence nodes i setup my target node and then i wire up arcs to all evidence nodes. The call to last couple of addarc functions throw a std ...

nah i am creating the network by calling AddNode/AddArc functions in my own code. i 'll double check where it is going off rails.

Hey Mate,

Please find attached the zip file with the network and a subset of data. The network snapshot i have attached along is trained over about 90,000 records (possibly repetitive), though the data file attached along contains around distinctive records only. Also since the original network ...

Firstly it's great to hear from you, thanks for your response.

When you said that you 'd like me to post my model did you mean like a dsl file or the actual code to build up and learn the smile model. I am happy to share my code the only issue is that it is in C++/CLI and a bit of C#, also it would ...

For the record i observed chunks take random time to be learned by em. Though i wonder how to know if the network is over trained . Is that possible ?

Hi All

Just a few quick questions to add to my initial post.

1- would explicitly setting up state names in the data set variables help speed up learning ?

2- Would attempting to learn dataset with multiple records be faster than learning 1 record at a time. I have been experimenting with ...

Hi All,
I have put together a belief network with about 2 dozen discrete nodes. 22 of these nodes are Boolean based, with 2 target nodes.
I intend of learning parameter weights by using DSL_em, I have all the data but it is taking for ever, to train the model. To be a bit more specific one record ...