BayesiaLabは、ベイジアンネットワーク技術を利用するためのツールキットです。BayesiaLabにより、ベイジアンネットワークの強力なパワーを容易に利用することが可能になります。
BayesiaLabは、手作業によるベイジアンネットワークの作成/編集機能、データからのベイジアンネットワークの自動抽出機能を持っています。データマイニングの強力なツールとしてもご利用いただけます。より詳細な機能については、下記の表を参照してください。
BayesiaLabには、スタンダード版とプロフェッショナル版の2種類があります。下記の表は、BeyesiaLabの機能のスタンダード版とプロフェッショナル版の比較表でもあります。
Functions |
Standard Edition |
Professional Edition |
Inference |
Exact Inference with Junction Tree |
X |
X |
Approximate Inference with Importance Sampling |
X |
X |
Interactive inference based on a file of observations |
X |
X |
Interactive Bayesian updating based on a file of observations |
X |
X |
Adaptive Questionnaire with respect to a target variable |
X |
X |
Adaptive Questionnaire with respect to a target modality |
X |
X |
Batch Labeling of the target variable |
- |
X |
Batch Inference of the Not Observable variables |
- |
X |
Batch Joint Probability |
- |
X |
Markov Blanket exportation (Available on subscription only) |
SAS Macro |
- |
X |
Data |
Data generation with MCMC |
- |
X |
JDBC/ODBC connection |
- |
X |
Database saving |
- |
X |
Imputation of the Missing Values |
- |
X |
Dictionaries |
Comments |
- |
X |
Colour categories |
- |
X |
Classes |
- |
X |
Observation cost |
- |
X |
Temporal indexes |
- |
X |
Modality values |
- |
X |
Modality names |
- |
X |
Exportation of the dictionaries |
- |
X |
Discretization of continuous variables |
Manual based on the repartition function |
X |
X |
Equal distances |
X |
X |
Equal frequencies |
X |
X |
Decision tree |
- |
X |
Aggregation of discrete modalities |
Manual |
X |
X |
Manual based on the correlation with a target variable |
X |
X |
Semi-Automatic wrt the correlation with a target modality |
X |
X |
Decision tree based on the correlation with a target modality |
- |
X |
Missing values processing |
Filtering |
X |
X |
Replacement |
X |
X |
Inference |
X |
X |
Association discovery |
EQ |
- |
X |
SopLEQ |
- |
X |
Taboo Search |
X |
X |
Taboo Order |
- |
X |
Target node Characterization |
Naive |
X |
X |
Augmented Naive |
X |
X |
Sons & Spouses |
- |
X |
Markov Blanket |
- |
X |
Augmented Markov Blanket |
- |
X |
Minimal Augmented Markov Blanket |
- |
X |
Semi-Supervised Learning |
- |
X |
Clustering |
Variable clustering |
- |
X |
Data clustering |
- |
X |
Multiple clustering |
- |
X |
Targeted Evaluation |
Multiple thresholds |
- |
X |
Global Precision |
X |
X |
Confusion Matrix |
X |
X |
Lift chart |
- |
X |
Gain chart |
- |
X |
ROC curve |
- |
X |
Automatic layout algorithms |
Symmetric |
X |
X |
Dynamic |
X |
X |
Genetic |
- |
X |
Mutual Information |
- |
X |
Random |
X |
X |
Graphical Network analysis |
Global strength of the Arcs |
X |
X |
Pearson's Correlation |
X |
X |
Target node |
X |
X |
Target modality |
X |
X |
Influence paths |
X |
X |
Causal analysis (essential graphs) |
X |
X |
HTML Analysis Report |
Arcs |
X |
X |
Target Node |
X |
X |
Evidence set (contradiction analysis) |
X |
X |
Special nodes |
Hidden |
X |
X |
Decision |
X |
X |
Utility |
X |
X |
Constraint |
X |
X |
Dynamic Bayesian networks |
X |
X |
Action policy learning |
Static Bayesian networks |
X |
X |
Dynamic Bayesian networks |
X |
X |
Graphics |
Histogram |
X |
X |
Repartition function |
X |
X |
Occurrence matrix with Khi2 test |
X |
X |
2D Scatter points |
- |
X |
3D Scatter points |
- |
X |
Bubble chart |
- |
X |
Multilingual |
English |
X |
X |
French |
X |
X |
Japanese |
X |
X |
Cross-Platform (Java technology) |
X |
X |