Comparing Simulation Performance for Crystal Ball, R, Julia and @RISK

The Need For Speed 2019
The Need for Speed 2019 study compares Excel Add-in based modeling using @RISK and Crystal Ball to programming environments such as R and Julia. All 3 aspects of speed are covered [time-to-solution, time-to-answer and processing speed] in addition to accuracy and precision.
  • 25 February 2019
  • Author: Eric Torkia
  • Number of views: 27026
  • Comments: 0

How I Learned to Think of Business as a Scientific Experiment

Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data
Imagine playing a game in which someone asks you to infer the number of sides of a polyhedron die based on the face numbers that show up in repeated throws of the die. The only information you are given beforehand is that the actual die will be selected from a set of seven die having these number of faces: (4, 6, 8, 10, 12, 15, 18). Assuming you can trust the person who reports the outcome on each throw, after how many rolls of the die wil you be willing to specify which die was chosen?
  • 6 November 2018
  • Author: Robert Brown
  • Number of views: 13402
  • Comments: 0
Perceptions and popularity of analytics technologies over time

Perceptions and popularity of analytics technologies over time

Will machine learning be the dominant technology focus over the next 2 years?

While doing some market analysis, we decided to take a look at how search terms were being used in Google around the world as they related to advanced analytics technologies. We picked five terms to compare over five years: risk analysis, big data, machine learning, Monte Carlo method, and forecasting. We then proceeded to download the data and apply some quick and dirty forecasting to see what would happen to the popularity of the search terms overtime.

  • 18 October 2017
  • Author: Eric Torkia
  • Number of views: 6338
  • Comments: 0