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- What's New in BayesiaLab 6?
- BayesiaLab

Introduction - Knowledge Modeling
- Discrete, Nonlinear, and Nonparametric Modeling
- Machine Learning
- Inference: Diagnosis, Prediction, and Simulation
- Model Utilization
- Knowledge Communication
- BayesiaLab Editions
- BayesiaLab Academic Edition
- BayesiaLab Education Package
- Bayesia Expert

Knowledge Elicitation

Environment - Bayesia Engine API
- Bayesia Market Simulator
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WebSimulator - Schedule BayesiaLab Demo

# 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:

- BayesiaLab Academic Edition - for individuals
- BayesiaLab Education Package - for classroom use

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 R^{2}) |
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 |