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Learn about the latest innovations in BayesiaLab 11
Version 11 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 once again features many innovations, including the native integration of a ChatGPT-based subject matter assistant. Here is a selection of the most important new features:
Hellixia is the name of BayesiaLab's subject matter assistant powered by ChatGPT (OpenaAI and Microsoft Azure). Hellixia offers a wide range of functions to help you characterize a given problem domain:
- Identify relevant dimensions of a problem domain
- Generate embeddings for learning a semantic network
- Generate meaningful descriptions for classes of nodes
- Provide tools for causal analysis
- Translate names and comments of nodes into different languages
- Generate images to be associated with nodes
The Independence of Causal Influence (ICI) tool has been enhanced with several updates:
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.
- 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.
- The Expert Editor has been rebranded 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 decide whether 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 option utilizes the current probability distribution of the node to craft an assessment linked to the specified expert.
- Delete Zero-Confidence Assessments: this option removes all assessments in which the expert's confidence level is set to 0.
- Delete Assessments: his feature deletes the assessments linked to the chosen 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.
The Formulas tab in the Node Editor now supports local variables.
Additionally, new functions have been introduced, with some of the most notable being:
TriangularMD(v1, x), i.e., triangular membership degree in fuzzy logic (under Special Functions)
Local variable ($V) and TriangularMD function
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).
SingleMode(v): A function designed to ascertain whether the distribution of variable v is unimodal (under Inference Functions).
Weight of Evidence now features four new types of analyses:
- Most/Least Relevant Explanations
- Most/Least Confirmatory Clues
The EQ-based learning algorithms are now disabled in scenarios where the score of an arc is not equivalent 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 with cycles.
- The data associated with the network can now be exported into an evidence scenario file.
- Scenarios are now editable, allowing adjustments to the index, weight, and comments.
The redesigned Target Evaluation function now features dedicated tabs for:
- Posterior Probabilities
In the 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 enhanced visualization flexibility.
- The 2D mapping 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.
Font Size proportional to Node Force
- The Node Analysis section has been enriched with the addition of numerous metrics, providing a more comprehensive analysis capability:
- Mutual Information with Target Node
- Mutual Information with Target State
- Bayes Factor
- Normalized Bayes Factor
- 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 associated with the nodes are now displayed when you hover over them.
- The option Hide Text for Ignored Nodes conceals the names of nodes that are not observable.
The new Dynamic Grid Layout is an innovative layout algorithm that is particularly suitable for generating readable graphs composed of badges with associated comments. It's particularly effective with graphs created with Hellixia.