Thinking of the possibilities for analyzing supply chain problems. Bayesian approaches. Has anyone examined? This shows some technical examples using R. Via DominoDataLab.
This guest post was written by Daniel Emaasit, a Ph.D Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel’s research interests include the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations. ....
This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice.
During my Masters in Transportation Engineering (2011-2013), I used traditional statistical modeling in my research to study transportation related problems such as highway crashes. When I started my PhD, I wanted to explore using machine learning because of the powerful academic and industry use cases I had read about. In particular, I wanted to develop methods that learned how people travel within cities, allowing for better planning of transportation infrastructure. ... "