BayesiaLab 是由 Bayesia 公司开发的目前最为成熟、完善的贝叶斯网络建模分析工具软件，采用图形化建模界面，操作简单直观，提供脚本以方便条件概率分布的设定，用户可以选择精确算法或近似算法，支持结构和参数学习，最重要的是该软件支持动态贝叶斯网络建模分析，更难得的是其界面十分精美。

产品特征：

网络模型： capitalize on your expertise and quantify your uncertainties
Thanks to its ergonomic interface, BayesiaLab enables you to easily formalize your knowledge in the form of bayesian networks.

Highly intuitive graphic development of networks: single click creation of nodes and “draw arcs”, arc inversion functions, magnetic grid and alignment tools helping you to position nodes effectively
Powerful equations editor for a quick description and concise relations (discrete and continuous probability distributions, arithmetic and trigonometric functions, ordinary operators)
Full copy/paste functions (clipboard system interoperability) and an import function for the main formats on the market
Learning/data mining: discover the knowledge buried in your data
Data import wizard allowing you to import from any source (text file, all the best known databases on the market)
Ability to filter variables or unused values, to discretize continuous variables, to incorporate discrete modalities
Very wide range of learning algorithms 

快速分析模型（Analyse your models at a glance）：
BayesiaLab makes it easier to understand your models, thanks to its wide range of varied analysis tools and its automatic node positioning algorithms. This means that you can discover the knowledge buried in your data by visually analysing the Bayesian networks obtained and through using the toolbox: 

Arc analysis: width related to the strength of the probabilistic relation
Analysis centred on a target value: characterization of the strength and the type of node relation in relation to the target modality
Target node modality analysis allows you to visualize two types of information for each node linked to its probabilistic relation with the target: the type of influence a variable has on a particular modality of the target variable, the gain in information provided by the node on the knowledge of the target modality
Causal analysis through the intermediary of important graphs
Analysis of contradictions in the set of observations
Generation of HTML reports on full target node analysis