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
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:
2420
Comments:
0
RSS