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What is BayesiaLab?

BayesiaLab 5.3

BayesiaLab 6 is a powerful Artificial Intelligence 6-01.pngsoftware (Win/Mac/Unix), which provides scientists a comprehensive “lab” environment for machine learning, knowledge modeling, analytics, simulation, and
optimization — all based on the Bayesian network paradigm.

BayesiaLab employs sophisticated learning algorithms to automatically generate structural models from data, making it a highly capable knowledge discovery tool. It enables applied researchers to explore high-dimensional problem domains like never before.

BayesiaLab's inference algorithms allow users to leverage Bayesian network models for complex evidential reasoning, even under uncertainty. In this context, BayesiaLab is unique in its ability to perform both observational and causal inference, facilitating a correct simulation of interventions in a system.

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What is a Bayesian Network?

A Conceptual Bayesian Network

A Bayesian network is a type of probabilistic graphical model, which can simultaneously represent a multitude of relationships between variables in a system. Bayesian networks are often also referred to as Bayesian Belief Networks (abbreviated as BBN), or just Bayes Nets.

The graph of a Bayesian network contains nodes (representing variables) and directed arcs that link the nodes. The arcs represent the relationships of the nodes.

Whereas traditional statistical models are of the form y=f(x), Bayesian networks do not have to distinguish between independent and dependent variables. Rather, a Bayesian network approximates the entire joint probability distribution of the system under study.

This allows the researcher to carry out "omnidirectional inference," i.e. to reason from cause to effect (simulation), or from effect to cause (diagnosis), all within the same model.

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The BayesiaLab Workflow in Practice

BayesiaLab is built on the foundation of the Bayesian network formalism, perhaps in the same way as a spreadsheet program is based on arithmetics.

BayesiaLab can generate Bayesian networks from human knowledge and/or by machine learning from data. The Bayesian network thus becomes a compact model of the underlying — often high-dimensional  problem domain.

Based on such a network model, BayesiaLab provides a wide range of analysis, simulation and optimization functions that allow the researcher to exploit all the dynamics captured in the network.


Free Book Download: Bayesian Networks & BayesiaLab

BayesiaLab BookA Practical Introduction for Researchers

This practical introduction is geared towards scientists who wish to employ Bayesian networks for applied research using the BayesiaLab software platform. Through numerous examples, this book illustrates how implementing Bayesian networks involves concepts from numerous disciplines, including computer science, probability theory, information theory, machine learning, and statistics. Each chapter explores a real-world problem domain, exploring aspects of Bayesian networks and simultaneously introducing functions of BayesiaLab. The book can serve as a self-study guide for learners and as a reference manual for advanced practitioners.

Please register to download the book (PDF, 382 pages, 41MB)

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