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Noisy Max and Noisy Or
Hi Mason,

Please click on the Probabilistic radio-button to indicate the equation is probabilistic.

Regards
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Noisy Max and Noisy Or

Hi Mason,

Please take a look at this link:  http://library.bayesia.com/display/BlabC/Discrete+Probability+Distributions

Remember that:

Noisy-Or:

This function may be used when there are several possible causes for an event, any of which can cause the event by itself, but only with a certain probability.
Also, if desired, it allows for the event to occur spontaneously, without any of the known causes being true.
It takes its simplest form when all the parent nodes and the child node are boolean (e.g. true/false).

It is similar to a regular “or” function in which the child takes on state true if any of its parents are true, otherwise it takes on false.
However, there is a probabilistic component (hence the term “noisy”), in that even if a parent is true, the child is not necessarily true.

For each parent there is one number called the causal strength, which gives the probability that the child is true when that parent is true, apart from any other interactions.
It is usually denoted pi for the  parent. As well, there is a probability that the child is true even when all the parents are false, called the leak probability (this can simply be made zero if there is no such possibility).

I hope this helps

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Data Import with MySQL SSL
Hi Craig,The SSL certificate path must be given as parameters in the command line used to run BayesiaLab.Here is a workflow to use SSL with MySQL and Java: (https://dev.mysql.com/doc/connector-j/5.1/en/connector-j-reference-using-ssl.html)The main steps are:1 - importing the certificate with the keytool.exe tool. You can find it, on Windows, in C:\Program Files\Bayesia\BayesiaLab\jre\bin2 - modifying the command line (or the shortcut) to add the following options to the JVM (Java Virtual Machine):-Djavax.net.ssl.keyStore=path_to_keystore_file-Djavax.net.ssl.keyStorePassword=password-Djavax.net.ssl.trustStore=path_to_truststore_file-Djavax.net.ssl.trustStorePassword=passwordSo the command line becomes (on Windows):[code:3m8dmoen]"C:\Program Files\Bayesia\BayesiaLab\jre\bin\javaw.exe" -Djavax.net.ssl.keyStore=path_to_keystore_file -Djavax.net.ssl.keyStorePassword=password -Djavax.net.ssl.trustStore=path_to_truststore_file -Djavax.net.ssl.trustStorePassword=password %BLAB_MEM% -jar "C:\Program Files\Bayesia\BayesiaLab\BayesiaLab.jar"[/code:3m8dmoen]3- running BayesiaLab and modifying the jdbc url to use SSL by adding some options:[code:3m8dmoen]jdbc:mysql://127.0.0.1:3306/sample?verifyServerCertificate=false&useSSL=true&requireSSL=true[/code:3m8dmoen](ref: http://www.razorsql.com/articles/mysql_ssl_jdbc.html)I hope this helps
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Inference on hierarchical data
Hi Andrea,After thinking about your problem, you should try the API BayesiaEngine.With it you could create several networks and make them interact with each others. http://www.bayesia.com/bayesia-engine-apiAnd this is the javadoc:http://library.bayesia.com/conflu/doc/You can take a look at a simple example here:http://library.bayesia.com/display/BlabC/Bayesia+Engine+API#ad83efae447640258f6bb70d78f9f013Don't hesitate to tell me if you have more questions.
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Best practices for using "small data"
Hi Steve,You can mix structure learning from data and expert modelling. In BayesiaLab, there are many tools allowing us to drive the learning process: - you can fix arcs before use a learning algorithm like Taboo or EQ - you can use local structural coefficients to increase or decrease the importance of a node - you can use the virtual number of states to modify the complexity of a node and make it more or less "attractive" for the other nodes - the global structural coefficient allows the learning algorithm to modify the global complexity of the network - the constraints on the arcs prevent the learning algorithm to add some arcs according to the given rules
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Evolutions and corrections
The WebSimulator has been updated today (2016-06-29).- Some minor tweaks and improvements were added.- The output component with probabilities displayed as text is now taken into account and well displayed in the left panel.
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Noisy Max and Noisy Or
Hi Guido,I'm sorry for the very late answer.The NoisyMax computes the probability distribution of a node by computing the probabilistic contributions of each parent and taking the max of these contributions according of the Noisy Max formula.To use this formula, we suppose that the states of the node represent an increasing sequence of values.The formula is the following:[latex:se0z1ujd]\begin{matrix} NoisyMax( & N, \\ & p_{1,1}, & ..., & p_{1,n}, & Test_{1},\\ & ..., & ..., & ..., & ..., \\ & p_{k,1}, & ..., & p_{k,3}, & Test_{k})\end{matrix}[/latex:se0z1ujd][latex:se0z1ujd]N[/latex:se0z1ujd] is the actual node with [latex:se0z1ujd]n[/latex:se0z1ujd] states.Each line represents the probability distribution given to [latex:se0z1ujd]N[/latex:se0z1ujd] if the [latex:se0z1ujd]Test_{i}[/latex:se0z1ujd] succeed.It must contains [latex:se0z1ujd]n[/latex:se0z1ujd] probabilities and the last term is the test itself.You can add as many tests as you want (here [latex:se0z1ujd]k[/latex:se0z1ujd] tests) following the same rule.For example, let a node [latex:se0z1ujd]A[/latex:se0z1ujd] with 3 states [latex:se0z1ujd]a_{1}, a_{2}, a_{3}[/latex:se0z1ujd] and with 2 parents [latex:se0z1ujd]B[/latex:se0z1ujd] with 2 states [latex:se0z1ujd]b_{1}, b_{2}[/latex:se0z1ujd] and [latex:se0z1ujd]C[/latex:se0z1ujd] with 2 states [latex:se0z1ujd]c_{1}, c_{2}[/latex:se0z1ujd]. [latex:se0z1ujd]\begin{matrix} NoisyMax( & A, \\ & 1, & 0, &0, & B == b_{1},\\ & 0.5, & 0.5, & 0, & B == b_{2},\\ & 0.1, & 0.8, & 0.1, & C == c_{1},\\ & 0, & 0.1, & 0.9, & C == c_{2})\end{matrix}[/latex:se0z1ujd]If [latex:se0z1ujd]B == b1[/latex:se0z1ujd] and [latex:se0z1ujd]C == c1[/latex:se0z1ujd], the computed distribution will be: [latex:se0z1ujd]\{ 0.1, 0.8, 0.1 \}[/latex:se0z1ujd]If [latex:se0z1ujd]B == b2[/latex:se0z1ujd] and [latex:se0z1ujd]C == c1[/latex:se0z1ujd], the computed distribution will be: [latex:se0z1ujd]\{ 0.05, 0.85, 0.1 \}[/latex:se0z1ujd]etc.Let [latex:se0z1ujd]N[/latex:se0z1ujd] a binary node with n binary parents [latex:se0z1ujd]N_{i}[/latex:se0z1ujd].The formula of the NoisyOr is:[latex:se0z1ujd]\begin{matrix} NoisyOr( & N, & leak,\\ & N_{1}, &p_{1}, \\ & ..., & ..., \\ & N_{k}, & p_{k})\end{matrix}[/latex:se0z1ujd]where [latex:se0z1ujd]p_{i}[/latex:se0z1ujd] is the probability of [latex:se0z1ujd]N[/latex:se0z1ujd] to be true, when its parent [latex:se0z1ujd]N_{i}[/latex:se0z1ujd] is true.Hope this helps,Mark
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Ignore Columns in Data Import
Hi Joe_Black,In the next release (6.0+) BayesiaLab will be able to import as many identifiers as wanted as separate columns.They will be kept separated when exporting results as well.
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show p-value not as %
Hi Krithika,We will mark this as an evolution for a new release.Mark
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BayesiaEngine - Licensing & Allowances, Tech Details
Hi Mike,I understand the point, however, as the terms of the license are, most of the time, adapted to the customer needs, it is not really possible to establish generic rules describing each kind of license.But, on the other hand, a more precise and clear description of what the license can allow would be great and could be established when asked by the customer.Mark
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