Search
Services
Documentation
RateSheet
Brochure
Risk + Analytics Training
Onsite Training
Live 1-on-1 Training
Course Outlines
Consulting
Analytics Strategy
Risk Modeling + Analysis
Remote Consulting
Project Risk Analysis
Store
Oracle Crystal Ball
Crystal Ball Standard
Crystal Ball Suite (OptQuest)
Crystal Ball FAQ
Full Catalogue
Simulation
Project Risk
Statistical Tools
Optimization
Forecasting
Palisade @RISK
Research
Crystal Ball User Guides
Articles on Analytics & Risk
Downloads
About Us
Company Profile
Business Team
Our Clients
Contact Us
Home
RESEARCH ARTICLES |
RISK + CRYSTAL BALL + ANALYTICS
Categories
0
RSS
Uncategorized
Expand/Collapse
59
RSS
Monte-Carlo Modeling
Expand/Collapse
68
RSS
Analytics Articles
Expand/Collapse
20
RSS
Engineering Modeling
Expand/Collapse
0
RSS
Best Practices
Expand/Collapse
1
RSS
Management Research
Expand/Collapse
Search
The Need For Speed 2019
Comparing Simulation Performance for Crystal Ball, R, Julia and @RISK
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:
261
Comments:
0
Bayesian Reasoning using R (Part 2) : Discrete Inference with Sequential Data
How I Learned to Think of Business as a Scientific Experiment
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:
612
Comments:
0
Bayesian Reasoning using R
Gender Inference from a Specimen Measurement
Imagine that we have a population of something composed of two subset populations that, while distinct from each other, share a common characteristic that can be measured along some kind of scale. Furthermore, let’s assume that each subset population expresses this characteristic with a frequency distribution unique to each. In other words, along the scale of measurement for the characteristic, each subset displays varying levels of the characteristic among its members. Now, we choose a specimen from the larger population in an unbiased manner and measure this characteristic for this specific individual. Are we justified in inferring the subset membership of the specimen based on this measurement alone? Baye’s rule (or theorem), something you may have heard about in this age of exploding data analytics, tells us that we can be so justified as long as we assign a probability (or degree of belief) to our inference. The following discussion provides an interesting way of understanding the process for doing this. More importantly, I present how Baye’s theorem helps us overcome a common thinking failure associated with making inferences from an incomplete treatment of all the information we should use. I’ll use a bit of a fanciful example to convey this understanding along with showing the associated calculations in the R programming language.
28 October 2018
Author:
Robert Brown
Number of views:
629
Comments:
0
RSS