Search found 430 matches
- Tue Nov 16, 2021 10:22 pm
- Forum: GeNIe
- Topic: GeNIe:Hybrid Models Heat Equations Autodiscretized Hybrid example
- Replies: 2
- Views: 1151
Re: GeNIe:Hybrid Models Heat Equations Autodiscretized Hybrid example
Hi, I will be happy to help. When the Season is not observed, you get a mixture of Gaussians in the node Outside Air Temperature, weighted by the probabilities of the four seasons. I can see in the picture that you must have observed a value -2.5 in the node Outside Air Temperature (there is a groun...
- Tue Oct 19, 2021 6:56 pm
- Forum: GeNIe
- Topic: Creating a node which sums the results of parent nodes' states
- Replies: 3
- Views: 4397
Re: Creating a node which sums the results of parent nodes' states
I'm afraid that this may be tricky. Being able to refer to the posterior probabilities of the parent nodes would amount to using the solution of a system of equations in the definition of some of the variables. I would suggest that you make all parent nodes and the child node continuous and then ref...
- Mon Sep 13, 2021 9:02 pm
- Forum: SMILE
- Topic: Posterior Probability Calculations
- Replies: 1
- Views: 3919
Re: Posterior Probability Calculations
SMILE unrolls a DBN and treats it as a static BN in which each of the time steps is represented explicitly. In this sense, given observations of any variables in any time steps, it calculates the conditional posterior probability distribution over the remaining variables. I believe that this procedu...
- Wed Sep 08, 2021 10:03 am
- Forum: GeNIe
- Topic: Why should virtual evidence sum to 1?
- Replies: 2
- Views: 2241
Re: Why should virtual evidence sum to 1?
Hi sverrevr, It's been a while since I looked at the issue -- we implemented virtual evidence in GeNIe and SMILE quite possibly 20 years ago :-). The best source of information will be articles that contain the precise definition and formulas. Here are a couple that you may try: https://www.research...
- Sat Jul 10, 2021 12:07 pm
- Forum: GeNIe
- Topic: Infer probability of a node given data file
- Replies: 1
- Views: 1987
Re: Infer probability of a node given data file
You can do what you want by using the "Validate..." functionality in "Learning" menu. You can designate the variable that you are interested in to be the class node and also produce an output file. The output file contains the inputs and then additional columns for each of the st...
- Thu Jun 24, 2021 9:55 pm
- Forum: SMILE
- Topic: Complex network +50 nodes
- Replies: 3
- Views: 4776
Re: Complex network +50 nodes
Hi, You can examine the machine diagnosis model interactively through our model repository. You can see each CPT when playing with the model. Here is the address of the repository, just in case you missed it: http://repo.bayesfusion.com/ The address provided to you by Tomek will lead you directly to...
- Wed Jun 23, 2021 8:12 pm
- Forum: SMILE
- Topic: Entropy reduction
- Replies: 15
- Views: 13041
Re: Entropy reduction
Hi Lotte, I'd love to help but I'm not 100% sure what cutting value you are referring to. Do you mean the value of cross-entropy that makes an observation worthwhile? I'm quite sure that GeNIe does not support any such threshold directly. As far as value of information in general is concerned, the c...
- Tue Jun 22, 2021 5:44 pm
- Forum: GeNIe
- Topic: Comparison and Value Node Alternative
- Replies: 2
- Views: 2234
Re: Comparison and Value Node Alternative
Hi Danny, Please look at the attached network -- it implements comparison between two distances. Generally, you will have to use equation nodes for the comparison. If you insist on having a discrete network (there is really no need for that, as GeNIe implements hybrid networks with no limitations on...
- Tue Jun 22, 2021 4:16 pm
- Forum: GeNIe
- Topic: Example network?
- Replies: 1
- Views: 2061
Re: Example network?
Have you tried our network repository? You can download all models from the repository directly from inside GeNIe: Just try "Open from BayesBox..." The default BayesBox address is the address of our model repository. All these networks are, I believe, also installed on your disk drive (ins...
- Tue Jun 08, 2021 8:58 pm
- Forum: SMILE
- Topic: Entropy reduction
- Replies: 15
- Views: 13041
Re: Entropy reduction
Hi Lotte, Somebody else at BayesFusion will answer your R question. Let me just comment on the entropy reduction question. The formula used in GeNIe/SMILE is somewhat different. We calculate cross-entropy, which is the expected difference between the entropy of the target and the entropy of the targ...
- Mon Jun 07, 2021 2:52 pm
- Forum: SMILE
- Topic: Entropy reduction
- Replies: 15
- Views: 13041
Re: Entropy reduction
Hi Lotte, We support entropy-based value-of-information calculation, which is based on cross-entropy. Cross-entropy between two nodes T and E (Target and Evidence) is the expected reduction of entropy of T given that you will observe E. You can play with this in diagnostic extensions of GeNIe, QGeNI...
- Sun May 30, 2021 9:21 pm
- Forum: GeNIe
- Topic: Learning parametes in DBNs
- Replies: 7
- Views: 3945
Re: Learning parametes in DBNs
Hi Liid, The colors of nodes are based on the maximum value of sensitivity (i.e., highest for any parameter). The SA algorithm performs calculation for all nodes because it is very, very efficient and it does not makes practical sense to run it on a subset of nodes when you can calculate sensitiviti...
- Thu May 27, 2021 7:27 am
- Forum: GeNIe
- Topic: Learning parametes in DBNs
- Replies: 7
- Views: 3945
Re: Learning parametes in DBNs
Hi Liid, You are right in that unrolling the network may be your only option. It is theoretically sound, as one way of conceptualizing inference in DBNs is that they are first unrolled. What will happen is that you may see the same patterns in various slices, depending of course what you select as t...
- Tue May 25, 2021 8:56 pm
- Forum: GeNIe
- Topic: Learning parametes in DBNs
- Replies: 7
- Views: 3945
Re: Learning parametes in DBNs
Hi Liid, Let me try to answer your questions one by one: (1) For learning DBN parameters, all your data should be in one file that follows the format described in GeNIe manual. In that file, you will have all time slices, i.e., ds0, ds1 and ds2. Otherwise, you are right that the ds0 data will be use...
- Mon May 24, 2021 4:54 pm
- Forum: GeNIe
- Topic: How can I better constrain distributions with extremely long tails?
- Replies: 3
- Views: 4376
Re: How can I better constrain distributions with extremely long tails?
Hi Camilla, I have always thought about this (i.e., modeling) as common sense, so nothing comes to mind in terms of the literature. As far as behavior of distributions with long tails goes, there is plenty of literature, including books -- a Google search will get you better results than what I can ...