What's New?

BayesiaLab 11.5.1

Version 11.5.1 of BayesiaLab is the latest iteration of our flagship product that has been under continuous development for nearly 25 years. No other organization has invested as many resources in developing technologies around the Bayesian network paradigm.

Release 11.5.1 once again features many innovations, including the native integration of a LLM-based subject matter assistant (OpenAI, OpenAI GPT Assistants, Azure, Mistral, Anthropic, Google AI, Llama API, Voyage AI).

Here is a selection of the most important new features:

Hellixia

Hellixia Menu

Hellixia is the name of BayesiaLab's subject matter assistant based on Generative AI. Hellixia offers a wide range of functions to help you characterize a given problem domain:

Automatic Causal Network Generator

• The Automatic Causal Network Generator creates a Causal Bayesian Network from your question by tapping into the knowledge available in LLMs.
• A Risk-Centric Causal Network option can be used to generate a causal network specifically focused on characterizing the risks associated with the criterion, including triggering events, control actions, risk consequences, and mitigation actions.
• In this context, Automatic Causal Network Generator creates nodes, adds explanatory comments for each causal link, retrieves beliefs about causal effects (with values ranging from -100 to 100), and constructs the conditional probability tables.
• The resulting causal Bayesian network is formatted automatically for easy interpretation.

Causal Network on Car Accidents

Risk-Centric Causal Network on Car Accidents

Automatic Semantic Network Generator

The Automatic Semantic Network Generator automatically extracts the main dimensions related to the user's question using a set of selected keywords. It then creates a dataset with the corresponding embeddings and applies machine learning to generate a network that reflects the semantic proximities between the dimensions.

Semantic Network on Car Accidents

Document Analysis

Four tools are available to perform a structured semantic analysis of your knowledge files (txt, pdf, mp3).

• Causal Network Generator: Create a Causal Network or Risk-Centric Causal Network from the knowledge file, using all the information contained in the file.
• Semantic Flowchart Generator: Creates a graph where the key semantic concepts extracted from the knowledge file are connected to represent their sequential relationships.
• Causal Semantic Diagram Generator: Creates a graph where the key semantic concepts extracted from the knowledge file are connected to represent their causal relationships.
• keyword Based Semantic Mapper: Creates a Semantic Network based on the keywords selected by the user to capture the main dimensions of the knowledge file.

Embedding Generator

This tool generates embeddings that capture the semantics of nodes using high-dimensional vectors. These vectors are stored in the data set associated with the graph, enabling the machine learning of semantic networks. If a data set already exists, the embeddings can either complete, replace, or be appended to it.

Dimension Elicitor

Identify key dimensions of a problem domain using a comprehensive set of keywords. For each identified dimension, it creates a corresponding node with a detailed description.

Comment Generator

Enhance node content by leveraging a comprehensive keyword set to identify relevant dimensions within the problem domain. These dimensions are then added as comments to the corresponding nodes.

Causal Network Generator

This tool develops a Causal Bayesian Network or a Risk-Centric Causal Network focused on the chosen node. It generates new nodes, provides detailed comments for each causal link explaining the mechanism, determines causal effects (with values ranging from -100 to 100), constructs conditional probability tables, and can estimate prior probabilities for root nodes.

Causal Relationship Finder

Similar to the Causal Network Generator, this tool designed to build a Causal Network using a predefined set of nodes instead of centering around a single node and generating new nodes.

Causal Network Priors Estimator

In the context of Causal Bayesian Networks, this feature estimates the prior probabilities of Boolean root nodes associated with your target node.

Causal Structural Priors

This tool evaluates whether the relationships represented by the selected arcs are causal. When causal relationships are identified, it generates Structural Priors, with values reflecting the confidence level in the existence of the relationship, along with comments describing the causal mechanisms. If the arcs follow the causal direction, the comments are associated with the arcs.

Pairwise Causal Link

This function evaluates whether a causal relationship exists between the two selected nodes and adds an arc if one is found. It quantifies the causal effect (ranging from -100 to 100), creates or updates the conditional probability table, and adds a comment to the arc explaining the causal mechanism. Additionally, it can generate the corresponding Structural Prior.

Doc-to-Node Generator

Creates a node for each selected knowledge file (txt, pdf, mp3), where the node name is the filename and the node comment is the content of the file. It can also generate a data set with the corresponding embeddings and apply machine learning to create a network that reflects the semantic proximities between the node names or comments.

Doc-To-Node

Semantic Flowchart Generator

Creates a graph where the key semantic concepts extracted from the text, which is composed of the selected node name, long name, comment, and/or knowledge file, are connected to represent their sequential relationships.

Semantic Flowchart on Car Accidents

Causal Semantic Diagram Generator

Creates a graph where the key semantic concepts extracted from the text—composed of the selected node name, long name, comment, and/or knowledge file—are connected to represent their causal relationships.

Causal Semantic Diagram on Car Accidents

Class Description Generator

Creates a class with the selected nodes and generates a descriptive summary of the corresponding factor based on the semantics of the associated nodes.

Semantic Variable Clustering

Create clusters (Factors) of nodes based on their semantic.

Image Generator

Produces icons that visually represent the information linked to the nodes.

Translator

Translates various network elements — including names of nodes, states, and comments on nodes and arcs — into the chosen language.

Report Analyzer

Processes the output from the Relationship Analysis Report, including arc and node forces, and generates an HTML report that outlines the key dynamics of the domain represented by the network.

Report Analyzer

Automatic Comment Summarization

Automatically generates long names that summarize the comments associated with the selected nodes (Node Contextual Menu).

Condense/Elaborate Comments

Condenses or elaborates the comments associated with the selected nodes (Node Contextual Menu).

Elaborate Comment

Speech-to-Text

Press the mic icon associated with any of BayesiaLab's text fields and use the Speech-to-Text feature to enter your text (node names, comments, ...).

Independence of Causal Influence

The Independence of Causal Influence (ICI) tool has been enhanced with several updates:

New Combination Functions

• SumPos(): An asymmetrical variation of the Sum function focusing on positive local mechanical effects.
• SumNeg(): A counterpart that emphasizes negative local mechanical effects.
• MinMax(): A function that implements the min method for negative values and the max method for positive ones.

ICI Wizard Enhancement

• A Condensed Display option has been introduced. This feature creates a network where the local effects are snapped to their parent and the combination nodes to their respective children.

Expert Knowledge Elicitation-Related Features:

• The Expert Editor has been renamed as the SMEs & BEKEE Session Manager.
• Subject Matter Experts (SMEs) can now be identified with specific colors for better differentiation.
• There's an option to to send out invitation emails to the SMEs.
• In terms of qualitative knowledge elicitation, specifically the qualitative segment of the Delphi Method, you can now utilize the Assessment Editor to produce Notes directly on the Graph Panel, derived from the comments provided by experts.

• When eliciting a node, its current distribution can be dispatched as a prior to all experts in BEKEE, serving as an alternative to the default uniform distribution.
• Node Contextual Menu:
  • Generate from Assessments: this function facilitates the creation of distributions based on the weighted votes of chosen experts.
  • Generate Assessments: This feature uses the node's current probability distribution to create an assessment associated with a selected expert. If Prior Weights are linked to the node, there's an option to use these weights to determine the expert's confidence level in the assessments.
  • Delete Zero-Confidence Assessments: this option removes all assessments in which the expert's confidence level is set to 0.
  • Delete Assessments: this feature deletes the assessments linked to the selected experts.
• Hellinger Distance: Measures the distance between experts' votes and a reference expert (usually the consensus).
• 2D/3D Mapping incorporates new metrics derived from experts' assessments.

Formulas

The Formulas tab in the Node Editor now supports local variables.

Additionally, new functions have been introduced, such as:

• TriangularMD(v1, x), i.e., triangular membership degree in fuzzy logic (under Special Functions)

• Deciban(x): The deciban is a logarithmic unit — much like the decibel or the Richter scale — introduced by Alan Turing for expressing probabilities. It is a tenth of a ban, which is also known as the base-10 log odds (under Arithmetic Functions)
• Hellinger(v1, v2): The Hellinger distance is a measure of the similarity between two probability distributions (under Inference Functions)
• NoisySum(s, leak, v1, w1, vn, wn): Used for representing situations where the variable s is the weighted (wi) sum of its parents (vi) plus an additional noise term (leak) to model uncertainty or random fluctuations
• DualNoisyOr(s, leak, c1, p1, cn, pn): This function implements a modified Noisy-Or model that operates based on the combined effect of all pi values. The parameters ci represent conditions or boolean variables, while pi are their associated effects (positive or negative). When the aggregated sum of pi values is positive, the function executes a Noisy-Or with an overall effect equal to this sum, effectively determining the probability of the True state. Conversely, when the sum is negative, the function applies the Noisy-Or logic to the False state, adjusting the likelihood of the outcome being False according to this negative sum
• SingleMode(v): A function designed to ascertain whether the distribution of variable v is unimodal (under Inference Functions).

Weight of Evidence

Weight of Evidence now features four new types of analyses:

• Most/Least Relevant Explanations
• Most/Least Confirmatory Clues

Structural Learning Algorithms

The EQ-based learning algorithms are now disabled in situations where the score of an arc is not the same in both directions. This can occur due to Filtered States, Constraints, Structural Priors, etc. The assumption of equivalence is no longer theoretically valid in such contexts and could result in invalid networks including cycles.

Evidence Scenario Files

• The dataset associated with a network can now be exported into an Evidence Scenario file.
• Scenarios are now editable, allowing adjustments to the index, weight, and comments.
• A new Evidence Scenario Report is now available, offering a detailed description of the scenarios' content.

Target Evaluation

The redesigned Target Evaluation function now features dedicated tabs for:

• Classification
• Posterior Probabilities
• Regression
• Triage

Graph Layout, Rendering and Editing

Dynamic Grid Layout

This innovative layout algorithm, particularly suitable for creating readable graphs featuring badges with associated comments, is also very practical for handling graphs created with Hellixia.

View Menu

Four new functions have been introduced to optimize the display of graphs. Users can now shrink or stretch graphs both vertically and horizontally, offering more flexibility in displaying the graph.

Position Menu

This new item has been introduced to enable the adjustment of the graphical layers of Nodes and Notes. It's available via their Context Menus.

Horizontal and Vertical Stacking

These new alignment tools enable the positioning of the selected nodes horizontally or vertically, aligning them with minimal space between them.

Highlight a Class

From the Context Menu of a Note, you can select a Highlight a Class and the Note will automatically adjust its size and position to include all nodes in the selected class.

Arc Editor

By double-clicking an arc, you can edit the text associated with the arc as well as its rendering properties.

Arc Comments

The width, layer, and position of Arc Comments can now be modified. Additionally, an automatic layout algorithm is available for positioning comments.

Color Link

This new feature, added to the Rendering Properties of Badges, Monitors, Bars, and Gauges, automatically applies a node's associated color to the Name Background Color. Additionally, it automatically selects white for the Name Color on dark backgrounds and black on lighter ones.

Selection Zone

By pressing Z, you can create a selection zone, in which objects are always selected regardless of their current state, i.e., selected or not-selected.

Numerical Evidence Entry for Gauges and Bars

A new approach is introduced for entering numerical evidence through shift+click on a node. Utilize the M and B icons to select the Distribution Estimation Method (MinXEnt and Binary, respectively), with the three icon colors representing the Observation Type: No Fixing, Fix Mean, and Fix Probabilities, respectively.

Pseudo Root-Nodes

If a node only has Function Nodes as parents, making it a root node of its subnetwork, and the parents of these Function Nodes have fixed observed values, then the distribution of these pseudo root-nodes is also automatically set to fixed.

Boolean Conversion

Featured in the Tools menu, this function enables the conversion of selected nodes into boolean nodes.

2D Mapping

2D Mapping function has been enhanced to incorporate an additional dimension for node analysis: Font Size, supplementing the existing Node Size and Color dimensions. This enables font sizes to be proportional to the selected metric.

Node Analysis

The Node Analysis section has been enriched with numerous additional metrics, further enhancing its analysis capability:

• Mutual Information with Target Node
• Mutual Information with Target State
• Bayes Factor
• Normalized Bayes Factor
• Kullback-Leibler Divergence
• Normalized Kullback-Leibler
• Total Effect on Target
• Standardized Total Effect on Target
• Direct Effect on Target
• Standardized Direct Effect on Target
• Number of Assessments
• Assessment Completion Rate
• Maximum Assessment Divergence
• Overall Assessment Divergence
• Missing Value Rate

Comments

Comments associated with nodes are now displayed as you hover over them.

Hide Text

The option Hide Text for Ignored Nodes conceals the names of nodes that are Not Observable.

Learn More

• Hellixia User Guide
• BayesiaLab User Guide