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With Open, you can select a Bayesian network file via a File Dialog and load it into Graph Window.
To the right of the file list, a preview panel shows you the structure of the Bayesian network to be loaded.
Additionally, you can specify what you wish to load along with the to-be-opened file. Clicking on the icons to the right of the file list allows you to toggle on and off specific file contents:
The Files of Type dropdown menu allows you to filter the types of Bayesian network formats to be displayed in the file list.
In addition to BayesiaLab's XBL format, select versions of BIF, NET, SSS, SCI, and DNE formats may be supported.
Bayesia does not guarantee the compatibility of BayesiaLab with any third-party or open-source Bayesian network formats.
Load the Dataset with the network.
Load the Evidence Scenario File with the network, if available.
Load the Junction Tree with the network, if available.
Load the Virtual Dataset with the network, if available.
Load the Simulator, i.e., load the WebSimulator configuration, if available.
Saves the Bayesian network in the active Graph Window using the XBL format.
By default, any dataset associated with the Bayesian network and any Evidence Scenario Files will be saved in the same XBL file so that they can be jointly loaded again later.
You can edit these default settings under Main Menu > Window > Prefences > Data
.
If the Bayesian network has a Junction Tree, it will be also automatically saved in the same file.
This command saves the current Bayesian network in a new file, adding an iteration number in parentheses to the current file name as a suffix.
If your current network is named Graph.xbl, the Increment & Save function will save it as Graph(2).xbl and not overwrite the original Graph.xbl file.
With each further iteration of Increment & Save, the counter in the suffix will increase by 1 unit, i.e., Graph(3).xbl, Graph(4).xbl, etc.
This is a helpful function for maintaining a history when developing a model, allowing you to revert to an earlier version when necessary.
The Network menu includes a range of standard functions related to:
Creating new files
Opening and closing files
Generating reports with network statistics
Clicking on the menu item Startup Page brings up a window featuring 12 quick-access cards.
The top row features some of the most common user actions after starting BayesiaLab:
Manually Create a Network
Open a Bayesian Network
Learn a Network from Data
Open the Media Center
The bottom two rows of cards show the most recently opened files with a network preview.
By default, the Startup screen is displayed right after launching BayesiaLab. The checkbox allows you to disable its automatic display.
Closes the active Graph Window and prompts you to save the corresponding file, if your network, the associated dataset, or the Evidence Scenario File were changed since the last Save operation.
Closes all open Graph Windows, except for the active one, and prompts you to save the corresponding files, if any of them were modified.
Closes all open Graph Windows and prompts you to save the corresponding files if any of them were modified.
Provides a list of the most recently opened networks so you can quickly reopen them as needed.
Set Working Directory allows you to define a Working Directory, i.e., a workspace, by associating a name with a specific directory.
Subsequently, you can recall the directories you defined with the menu item Recent Working Directories.
With Recent Working Directories, you can quickly recall a Working Directory you previously specified.
The list features the name you assigned plus the corresponding path.
The size of this list can be modified under Main Menu > Window > Preferences > Menus. See Recent Networks.
Closes all graphs, prompts you to save if needed and closes BayesiaLab.
Allows exportingtheMarkovblanketofthetargetvariable of the current network into a language selected in the following dialog box:
Once the network is exported in a language, it can be used to infer the value of the target variable according to the observations of the other variables.
Allows locking the network with a password to prevent it from being edited. Then, the network can be used only in Validation Mode. This menu gives access to the lock manager.
Prints the Bayesian network of the active graph window. An assistant gives access to:
the setup of the page-setting,
the configuration of the printer,
the selection of the desired scale for the network,
the possibility of displaying reference marks. These marks are useful when the network has to be print on more than one page. They indicate the page number (column, row), the border, and the vicinity,
the possibility to center the network.
Save As lets you choose a new file name and location for your current Bayesian network.
Additionally, you can specify what you wish to include in the to-be-saved file. Clicking on the icons to the right of the file list allows you to toggle on and off specific contents:
The size of this list can be modified under Main Menu > Window > Preferences > Menus.
Enter your desired Working Directory name into the Name field and select the corresponding Path using the Directory dialog .
Clicking Recent Working Directories opens up a list of the most recently used Working Directories, from which you can pick the one you wish to recall.
Save the Dataset with the network.
Save the Evidence Scenario File with the network, if available.
Save the Junction Tree with the network, if available.
Save the Virtual Dataset with the network, if available.
Save the Simulator, i.e., load the WebSimulator configuration, if available.
Closes the active Graph Window and prompts you to save the corresponding file if your network, the associated dataset, or the Evidence Scenario File were changed since the last Save operation.
This password locking mechanism allows you to share your networks to make sure they will not be modified by unauthorized users.
When a network is locked, you cannot validate and save the modifications done in the Node Editor, add or delete arcs and nodes, associate dictionaries and databases for learning, modify classes, etc.
However, you can still edit the costs associated with the nodes as they are utilized only in the Validation Mode (e.g, Adaptive Questionnaire, not observable nodes, etc.).
To start protecting a network select Main Menu > Network > Protect
.
Unless the network already has a lock, the following dialog box is displayed:
When the network is unlocked, the menu Network | Lock displays the following dialog box:
This dialog box allows you to:
lock the network using the existing password,
remove completely the Lock,
change the Lock Password.
Upon confirmation of your password, the Lock icon appears in the Status Bar to indicate that the network is unlocked.
You just have to click on the icon to lock/unlock the network. The icon updates to to indicate that the network is locked.
The Reports submenu within the Network menu offers an array of information about the Bayesian network in the active .
The Network Comments Report displays the information recorded in a network's field.
And if available, the Network Comments Report also lists the associations of .
The Network Report is a very comprehensive documentation of the network in the active Graph Window.
It includes statistics about the network structure as a whole, plus details for each node, such as the Node States, the Conditional Probability Tables, and equations.
As such, it presents all qualitative and quantitative knowledge contained in the network as a long, tabular report.
To some extent, you could recreate the network from all these details.
Select Main Menu > Network > Reports > Network
to create the Network Report.
The report can be quite substantial, depending on your network's size and complexity.
The following screenshot only shows the top portion of a much longer report:
For a thorough offline analysis, you may want to save the Network Report as an HTML file, which you can then open as a spreadsheet in Excel.
Occurrences refer to the number of observations in a cell of a Probability Table or a Conditional Probability Table.
The number of cells in a Conditional Probability Table is a function of the following parameters:
The number of Parent Nodes.
The number of Node States of the Parent Nodes.
The number of Node States of the Child Nodes.
Here, Age is discretized into 4 states and BMI into 6 for a total of 48 cells in the table associated with BMI.
The numbers in each cell are counts of observations or Occurrences. In our case, each Occurrence represents one person from the sample of 200 individuals.
For instance, the Occurrence table associated with BMI states that Count(BMI≤20 | Age≤30)=2. So, we have only two Occurrences of that particular condition, i.e., only two individuals who are 30 years old or younger have a BMI of 20 or lower.
To create a Bayesian network, BayesiaLab needs to translate the Occurrences in each cell into probabilities.
However, with a small number of Occurrences, that can become an issue.
We have repeatedly referenced a rule of thumb, which says that we should have a minimum of 5 Occurrences per cell to estimate a Probability Table or Conditional Probability Table reliably.
In our example, several cells fall below the recommended minimum.
Such deficiencies are easy to recognize in a small example, but in more complex networks, it can be difficult to spot such weaknesses.
That is the motivation for the Occurrence Report. It displays all tables in a network and visually highlights potentially problematic cells with low Occurrences.
Select the nodes you want to include in the Occurrences Report. I none are selected, the analysis will be performed on all nodes.
Select Main Menu > Network > Reports > Reports> Occurrences
to create the Occurrences Report.
The Occurrence Report opens up and shows all Probability Tables and Conditional Probability Tables.
The fields in the report are color-coded to highlight potential issues:
Cells with 0 Occurrences are marked in red.
Cells with 5 Occurrences are marked in yellow. This is generally considered the minimum number of Occurrences.
Cells with 40 or more Occurrences are marked in green.
Furthermore, the Occurrence Report calculates the mean number of Occurrences for each row in all Probability Tables and Conditional Probability Tables.
If the mean value of any row in any of the nodes drops below the threshold of 5, the corresponding nodes are called out at the top of the report.
Whenever you learn a Bayesian network from a small dataset, you must consider whether the number of observations is sufficient for correctly estimating all Probability Tables and Conditional Probability Tables in the network.
For instance, using the Occurrences Report, you can evaluate whether all Conditional Probability Tables in your network meet the rule-of-thumb criterion of at least 5 observations per cell.
For a deeper analysis, BayesiaLab can produce the Confidence Intervals Report, which we discuss on this page.
To understand how Confidence Intervals can be computed, we first need to explain the estimation of probabilities in the Probability Tables and Conditional Probability Tables, the so-called parameters.
In BayesiaLab, these parameters are estimated using Maximum Likelihood, i.e., using the frequencies observed in the dataset:
where:
So, the Parameter Estimation is straightforward and happens entirely in the background in BayesiaLab.
As a result, we may not always be aware of what numbers gave rise to the probabilities we see in a Probability Table or Conditional Probability Table, as the following diagram illustrates:
However, in terms of our confidence in the estimate, the two approaches are not the same. Our intuition tells us that we should have more confidence in the 0.1 value calculated based on the sample of 10,000.
BayesiaLab is using precisely the same approach for the Confidence Intervals Report.
However, in BayesiaLab, you can avoid resorting to this heuristic by using Uniform Prior Samples.
Within this network, focus on the three nodes BMI, Age, and Gender:
Go to Main Menu > Network > Reports > Confidence Intervals
to start the Confidence Intervals Report.
The Confidence Interval Report window opens up.
At the top of the report, the Confidence Level that serves as the basis for the reported Confidence Intervals is displayed.
Then, for each node, one table is shown.
For each cell containing a parameter estimate, an adjacent cell to the right displays the corresponding Confidence Interval in percentage points.
The color-coding scheme is identical to the one used in the Occurrences Report.
The fields in the report are color-coded to highlight potential issues:
Cells with 0 Occurrences are marked with a red background.
Cells with 5 Occurrences are highlighted with a yellow background. This is generally considered the minimum acceptable number of Occurrences.
Cells with 40 or more Occurrences are marked with a green background.
You can adjust the Confidence Level used for this report.
Go to Main Menu > Window > Preferences > Tools > Statistical Tools
.
Select the desired value from the Confidence Level dropdown menu.
Note that your selection here also applies to all other statistical tools and tests used in BayesiaLab.
Network Comments provides space for notes, descriptions, and references regarding a Bayesian network.
In the Network Comments field, you can enter and edit paragraph-style text.
You can access the Network Comments Editor in two ways:
Main Menu > Network > Properties > Comments
.
Graph Panel Context Menu > Properties > Comments
.
A new window opens featuring the Node Comments Editor.
By default, the Node Comments field contains the date and time the file was created, plus the user that created the file.
Alternatively, the Network Comments field displays any custom text you may have defined, such as a problem domain description.
You can apply HTML-style formatting to your text using the toolbar, including links and images.
Note that Network Comments are automatically saved with the network file.
If you share your network file with others, the information contained in Network Comments will be accessible to them.
The following example with one Parent Node (Age, measured in years) and one Child Node (BMI, i.e., Body Mass Index, measured in ) illustrates this with numbers:
The affected nodes in the Graph Panel are also marked with the information icon .
is the estimated probability,
is the state of variable ,
represents the number of occurrences of the argument in the data set.
So, BayesiaLab could have estimated a probability of 0.1 (or 10%) for in numerous ways, e.g., based on a sample of 10 or 10,000: .
From Frequentist Statistics, we know how to calculate a Confidence Interval
for a proportion in a sample, which is exactly what the parameter represents.
So, for a Confidence Level of 95%, the Confidence Interval is calculated as:
where
If zero observations were observed for a given state, e.g., , the Rule of Three would have to be used instead to produce Confidence Intervals:
To illustrate the Confidence Intervals Report, we use the following network: NHANES_DEMO_BMX.xbl