Bayesian Networks Bring Order to Complex Relationships

They not only capture random variables but also their effects on each other

10-21-2020
Don York
SYSTEMS ENGINEERING

I stood in my basement staring at the crack in my foundation that I had sealed last summer, as torrential rains descended. The incoming brown liquid mocked my efforts at foundation repair. So, I braved the 90-degree weather and crawled under my deck, armed with my short-handled shovel and heavy-duty power drill. For several hours I labored through solid, concrete-like clay to dig a deep-enough hole to access the exterior of the foundation in order to seal the culprit.

Because I am an engineer, my mind turned to probabilities. What was the probability that this crack would have leaked again? What was the probability that it would have rained this much? What was the probability that the local weather forecaster was ever right?

Making sense of chaos

The forecasters have to predict the weather while trying to factor in temperature, humidity, precipitation, cloud cover, wind speed, wind direction, jet streams, cold fronts, warm fronts, and more. These seemingly random factors combine to cause weather (and my leaky basement).

Many years ago, one of my systems engineering professors discussed chaos theory, describing how systems, like the weather, are sensitive to and dependent on initial conditions, i.e., what happens first. He told us about the “butterfly effect,” in which a small change in the state of a system can result in differences in a later state.

For example, the flapping of the wings of a butterfly in China can affect the weather thousands of miles away in New York a few weeks later. Really? What’s the probability that a butterfly flapping its wings could influence weather thousands of miles away?

Don't let this happen to you.

Bayesian networks to the rescue

The good news is that there is an engineering concept for that! Thomas Bayes was an English statistician, philosopher, and Presbyterian minister who lived in the early 1700s and is known for formulating the theorem that bears his name. A Bayesian network is a model representing a set of random variables, like those that affect the weather. Not only does it capture a set of random variables, but it also describes their conditional dependencies on one another.

For example, given the conditions that there is complete cloud cover and the humidity is 100 percent, what is the probability that it will precipitate? This probability is based on conditions and called conditional probability. Bayes translated a set of complex relationships or dependencies into an intuitive, mathematic model.

Bayesian network models incorporate uncertainty, and they work in the face of missing or inconsistent data. Sounds like a weather forecaster’s dream to me! Not only do forecasters use Bayesian networks, but SAIC uses them, too. Our robust systems engineering is underpinned by mathematical methods. Recently I was part of a team that developed a method to probabilistically determine integration readiness in complex systems using a Bayesian network model.

Our method may not save me a call to the handyman, but chances are my next engineering job will have a lot less risk.

FURTHER READING: More from our systems engineering expert Don York.

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Systems Readiness: Are We Really Ready?

Posted by: Don York

Senior Systems Engineer

Don York is a Senior Systems Engineer at SAIC.

Dr. York is a SAIC Fellow with more than 40 years of experience in the design, development, engineering and productization of advanced technology systems.

He is recognized as the go-to professional for systems engineering, applying systems engineering expertise across various domains.

As a principal technical expert for system development metrics, Dr. York is part of a team that helped to develop the Government Accountability Office’s Technology Readiness Assessment Guide, published in 2019.

Dr. York has made contributions to the field of Systems Engineering in education, the profession, and the community. He has taught Systems Engineering at the graduate level and has co-authored and presented numerous technical papers.

He holds his Bachelors and Masters in Mechanical Engineering, a PhD in Systems Engineering and is a Certified Systems Engineering Professional.

Dr. York has been a member of the International Council on Systems Engineering (INCOSE) for over 25 years where he currently serves as the Chairman of INCOSE’s Corporate Advisory Board.

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