RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

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: 102
  • Comments: 0

Gender Inference from a Specimen Measurement

Bayesian Reasoning using R
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: 115
  • Comments: 0

A structured way to make sure you got all the info you need

Are you asking the right questions?
Whether you are a businessman or a practicing professionals such as an attorney, a doctor or a consultant, the ability to ask the right questions is imperative along with the ability to capture the information that is important when an answer is provided. Sometimes knowing where to start is the toughest aspect of solving a problem. Usually a sound approach is breaking out complex problems into smaller more manageable components; as the old adage goes “Do you know how to eat an elephant? One bite at a time!” Check out how to break-down tough problems by following the simple 5W question framework.
  • 28 October 2018
  • Author: Eric Torkia
  • Number of views: 100
  • 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: 92
  • Comments: 0
Multi-Dimensional Portfolio Optimization with @RISK

Many speak of organizational alignment, but how many tell you how to do it? Others present only the financial aspects of portfolio optimization but abstract from how this enables the organization to meets its business objectives. We are going to present a practical method that enables organizations to quickly build and optimize a portfolio of initiatives based on multiple quantitative and qualitative dimensions: Revenue Potential, Value of Information, Financial & Operational Viability and Strategic Fit.

This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover how this approach can dramatically improve organizational focus and overall business performance.

We will discuss these topics as well as present practical models and applications using @RISK.

  • 28 June 2016
  • Author: Eric Torkia
  • Number of views: 114
  • Comments: 0
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