14 KiB
A Tutorial on Learning With Bayesian Networks - Microsoft Research
Try Microsoft Edge A fast and secure browser that's designed for Windows 10 No thanks Get started
This site uses cookies for analytics, personalized content and ads. By continuing to browse this site, you agree to this use. Learn more
Microsoft Research
- Microsoft 365
- Azure
- Office 365
- Dynamics 365
- SQL
- Windows 10
- More
- Products & Services
- Emerging Technologies
- Developer & IT
- Partner
- Industries
- Other
- View all
Research areas
- Intelligence
- Systems
- Theory
- Other Sciences
Programs & Events
Blogs & Podcasts
Labs & Locations
- Microsoft Research
- Other labs/locations
- About
A Tutorial on Learning With Bayesian Networks
March 1, 1995
Authors
- David Heckerman
Publication Type
TechReport
Pages
57
Number
MSR-TR-95-06
Abstract
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.
Related Info
Research Areas
Research Labs
Follow Microsoft Research
-
Share this page
-
What's new
- Surface Book 2
- Surface Pro
- Xbox One X
- Xbox One S
- VR & mixed reality
- Windows 10 apps
- Office apps
Store & Support
- Account profile
- Download Center
- Sales & support
- Returns
- Order tracking
- Store locations
- Support
- Buy online, pick up in store
Education
- Microsoft in education
- Office for students
- Office 365 for schools
- Deals for students & educators
- Microsoft Azure in education
Enterprise
- Microsoft Azure
- Enterprise
- Data platform
- Find a solutions provider
- Microsoft partner resources
- Microsoft AppSource
- Manufacturing & resources
- Financial services
Developer
- Microsoft Visual Studio
- Windows Dev Center
- Developer Network
- TechNet
- Microsoft Virtual Academy
- Microsoft developer program
- Channel 9
- Office Dev Center
Company
- Careers
- About Microsoft
- Company news
- Privacy at Microsoft
- Investors
- Diversity and inclusion
- Accessibility
- Security
- Sitemap
- Contact us
- Privacy & cookies
- Terms of use
- Trademarks
- About our ads
- © Microsoft 2018