BayesFusion Support Forum

Hi lir24,

What type of errors are you getting?

Martijn

Hi Lihua,

On our download page you can find a version for IOS under "Unix like"

Best,

Martijn

In this case we calculate classification accuracy for each network structure that we try and pick the one with the highest accuracy. This is not really a typical way of doing it, but useful when learning networks for classification. Here the score represents the number of correct classifications don...

No, that's a too simplistic view. Consider building a classifier that has a class variable and a few feature variables. Learning parameters for this model would give you a likelihood score. I can guarantee you that if you drop the feature variables and you learn parameters for just the class variabl...

Hi Ind,

The score is a loglikelihood.
Since the likelihood is a number between 0 and 1 the log likelihood will be negative.
For the loglikelihood, higher (in this case, closer to 0) is better.

Best,

Martijn

Hi,

We do not have a Python wrapper available.
Not sure if anyone ever created one on their own.

Best,

Martijn

This has been included in the library.

Best,

Martijn

Hi, Did you read my email? Currently VirtualEvidence is not supported in the wrapper. We would have to add it, but this may take a little bit. You have the option of extending the wrapper yourself if you are really in a hurry. See http://genie.sis.pitt.edu/wiki/Appendices:_Extending_SMILE.NET_Librar...

Depends on your model structure, but assuming you set evidence for "evidence nodes" and are looking at the "hidden nodes", I'd say yes if you are looking a the hidden node x in time slice 5.

Hi Andrew, The grey color isn't really a new default color, it actually has a specific meaning. It means that the child node is in fact conditionally independent of the parent. If you mouse over the arc (the arrow head works best for this), you'll see it's labeled as unnecessary. If you make any cha...

P(Zt|Zt-1....Zt-k)= *i=1-n P(Zit|Pa(Zit)) Murphy's algorithm works with DBNs that adhere to the first order Markov assumption, in GeNIe we allow for higher order networks, meaning that we allow temporal arcs to go further than just the next timestep. That's what this equation means. and the initial...

saifjamal wrote:can i have any documentation on how the polytree working with the rolled up network?

No inference is ever performed on a rolled up DBN.

We always unroll first

Martijn

The inference results will be the same. The difference is in the amount of necessary space needed for doing the inference. We unroll the network, which means we need space for the whole network. Murphy's algorithm just needs space for both time slices (the junction trees for the two slices, to be ex...

...where in the backward pass of polytree algorithm at time t will update the belief of the hypothesis node in the previous time slice. which is the cause they call it special case of HMM. You have to understand that you have to drop the references to HMM and it's algorithm. It's irrelevant. We unr...

is the first order temporal arc means that the relationship between two time slices modeled by first order hidden markov model. thats what made me confused A Hidden Markov Model is a special case of a Dynamic Bayesian network. It only has temporal arcs of order 1, meaning that nodes with temporal a...