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BayesiaLab

BayesiaLab Editions

Beyond the core product, BayesiaLab Professional Edition, two further BayesiaLab editions are available, the Standard Edition and the Analyst Edition.

The BayesiaLab Standard Edition was developed primarily for expert knowledge modeling, i.e. building Bayesian networks manually. As such, the Standard Edition only has a small subset of the machine learning and analytical capabilities of the Professional Edition.

The BayesiaLab Analyst Edition is a “player” version of the BayesiaLab Professional Edition. It provides read-only access to previously generated XBL files, while providing a large range of analysis, inference and simulation tools. This edition does not allow access to datasets. This is ideally suited for researchers who wish to share their models with a broader audience, while preventing any modifications to the original model.

Additionally, for students and faculty of accredited academic organizations, we offer economically-priced configurations of BayesiaLab: 

For further details on licensing options and pricing, please download the complete price list.

Feature Comparison

Functions Standard Edition Professional Edition Analyst Edition
Inference Exact Inference with Junction Tree n n n
Approximate Inference with Importance Sampling n n n
Causal Inference n n n
Interactive inference based on Evidence Scenario file or on the current database n n Scenario file only
Interactive Bayesian updating based on Evidence Scenario file or on the current database n n Scenario file only
Adaptive Questionnaire with respect to a target variable/target state n n n
Batch Labeling of Target Variable - n -
Batch Inference of Not Observable Variables - n -
Batch Labeling of Target Variable with Most Probable Explanation (MPE) - n -
Batch Inference of Not-Observable variables with the MPE - n -
Batch Joint Probability - n -
Batch Likelihood - n -
Knowledge Elicitation Environment Direct Knowledge Assessment n n -
Online Knowledge Assessment ¢ ¢ -
Assessment Sensitivity Analysis n n n
Assessment Report n n n
Markov Blanket Export SAS® Macro - ¢ ¢
JavaScript - ¢ ¢
PHP - ¢ ¢
R - ¢ ¢
Data Data Generation with MCMC - n -
JDBC/ODBC connection - n -
Database Saving - n -
Missing Value Imputation - n -
Weights n n -
Stratification - n -
Data Type (learning/test) n n -
Row Identifiers n n -
Import/Export Dictionaries - n n
Discretization of Continuous Variables Manual, based on the repartition/density function n n -
Equal Distances n n -
Equal Frequencies n n -
K-Means n n -
Decision Tree - n -
Density Approximation - n -
Re-Discretization - n -
Aggregation of Discrete Modalities Manual n n -
Manual, based on correlation with a target variable n n -
Semi-Automatic, based on correlation with a target state n n -
Decision Tree based on the correlation with a target state - n -
Missing Values Processing Filtering n n -
Replacement n n -
Inference n n -
Unsupervised Structural Learning Maximum Weight Spanning Tree n n -
EQ - n -
SopLEQ - n -
Taboo Search n n -
Taboo Order - n -
Supervised Learning Naïve n n -
Augmented Naïve n n -
Tree Augmented Naïve n n -
Sons & Spouses - n -
Markov Blanket - n -
Augmented Markov Blanket - n -
Minimal Augmented Markov Blanket - n -
Semi-Supervised Learning - n -
Clustering Variable Clustering - n -
Data Clustering (EM/K-Means, Binary) - n -
Multiple Clustering (EM, Binary) - n -
Targeted Evaluation Multiple Thresholds - n -
Global Precision n n -
Pearson Correlation Coefficient (R and R2) n n -
Confusion Matrix n n -
Lift Chart - n -
Gain Chart - n -
ROC Curve - n -
Global Evaluation Log-Likelihood n n -
Contingency Table Fit n n -
Extract Database based on Likelihoods - n -
Automatic Layout Algorithms Symmetric n n n
Dynamic n n n
Radial - n -
Genetic - n -
Distance Mapping (Mutual Information/ Pearson) - n -
Grid n n n
Staggered - n -
Random n n n
Network Analysis - Visual Arc Force n n n
Arcs’ Mutual Information n n n
Pearson’s Correlation n n n
Node Force n n n
Correlation with the Target Node/State n n n
Neighborhood Analysis n n n
Mosaic Analysis n n n
Mapping Reference State - Mutual Information only n Reference State - Mutual Information only
Influence Analysis w.r.t. Target Node n n n
Target Sensitivity Analysis n n n
Target Direct Effect Analysis - n -
Target Mean Analysis - n -
Target Mean Direct Effect Analysis - n -
Parameters Sensitivity analysis n n n
Most Probable Explanation n n n
Influence Paths n n n
Causal Analysis (Essential Graphs) n n n
Network Analysis -Report Correlations with the Target Node n n n
Conditional Mean Analysis - n -
Target Dynamic Profile - n -
Resources Allocation Optimization - n -
Total Effects on Target n n n
Direct Effect on Target - n -
Contribution Analysis - n -
Probability Analysis w.r.t. to the Target State n n n
Difference Decomposition Analysis - n -
Evidence Analysis n n n
Relationships Analysis n n n
Information Analysis - n -
Hidden Variable Discovery - n -
Network Analysis Target Optimization n n n
Trees Target Optimization - n -
Target Interpretation n n n
Tools Network Comparison n n n
Variable Clustering Comparison - n -
Distribution Comparison - n -
Cross-Validation - n -
Multi-Quadrant Analysis - n -
Time Series - n -
Evidence Instantiation - n -
Design of Experiments - n -
Export Network by Expert n n -
Export Probability Assessments - n -
Export Expert Assessments - n -
Special Nodes Hidden n n -
Decision n n n
Utility n n n
Constraint n n n
Dynamic Bayesian Networks n n n
Action Policy Learning Static Bayesian Networks n n n
Dynamic Bayesian Networks n n n
Graphics Histogram n n -
Occurrence Matrix n n -
Distribution Function n n -
Box Plot - n -
2D Scatter Plot - n -
3D Scatter Plot - n -
Bubble Chart - n -
Language Versions English n n n
French n n n
Cross-Platform (Java Technology) n n n
n Feature included in price
Feature not included or not available
¢ Feature available as part of an additional subscription

BayesiaLab Price List

Please Register to Download the Complete Price List (PDF, 21 pages, 2.2MB)