In this webinar, we develop a WebSimulator that allows decision makers to experiment with assumptions for business planning purposes, such as sales forecasts, inventory levels, cost estimates, etc. More specifically, we create a Bayesian network model that explicitly accounts for the uncertainty in all assumptions as opposed to utilizing single-point forecasts. This Bayesian network serves as the inference engine that drives the WebSimulator output.
Given the Bayesian network, we can also use BayesiaLab's Policy Learning function to formally search for optimal decisions in the presence of uncertainty. Importantly, any such "machine-learned solutions" can be easily replicated by stakeholders, who can individually try out various alternative assumptions and policy scenarios in the WebSimulator.
- Presentation Slides (PDF, 15 MB)
- Case Study Demo Network (XBL, 291 KB)
- Case Study Demo Network, Base & Premium Scenario (XBL, 190 KB)
- Final WebSimulator: https://simulator.bayesialab.com/#!simulator/207374505016
"Version 1" of the WebSimulator turned out to be a huge success, and it rapidly became an integral part of research workflows. With its growing popularity, however, BayesiaLab users have been developing applications that required greater flexibility and a more sophisticated web interface for the end user.
With the recent release of BayesiaLab 8, we also introduced an updated WebSimulator. Entirely new is the WebSimulator Editor inside BayesiaLab 8, which allows you to design and configure an elaborate web interface with many customizable elements, including bar charts, gauges, etc. Plus, you can immediately review its final appearance with a new preview function.
In this webinar, we will demonstrate all the new features of the WebSimulator by taking you through a complete workflow, from model development to publishing the model via the Bayesia WebSimulator Server.