Markov: Maths for the Newspaper Reader

September 14, 2017

Remarkable. I read a pretty good write up called “That’s Maths: Andrey Markov’s Brilliant Ideas Are Still Bearing Fruit.” I noted the source of the article: The Irish Times. A “real” newspaper. Plus it’s Irish. Quick name a great Irish mathematician? I like Sir William Rowan Hamilton, who my slightly addled mathy relative Vladimir Igorevich Arnold and his boss/mentor/leader of semi clothed hikes in the winter Andrey Kolmogorov thought was an okay guy.

Markov liked literature. Well, more precisely, he liked to count letter frequencies and occurrence in Russian novels like everyone’s fave Eugene Onegin. His observations fed his insight that a Markov Process or Markov Chain was a useful way to analyze probabilities in certain types of data. Applications range from making IBM Watson great again to helping outfits like Sixgill generate useful outputs. (Not familiar with Sixgill? I cover the company in my forthcoming lecture at the TechnoSecurity & Digital Forensics Conference next week.)

I noted this passage which I thought was sort of accurate or at least close enough for readers of “real” newspapers:

For a Markov process, only the current state determines the next state; the history of the system has no impact. For that reason we describe a Markov process as memoryless. What happens next is determined completely by the current state and the transition probabilities. In a Markov process we can predict future changes once we know the current state.

The write up does not point out that the Markov Process becomes even more useful when applied to Bayesian methods enriched with some LaPlacian procedures. Now stir in the nuclear industry’s number one with a bullet Monte Carlo method and stir the ingredients. In my experience and that of my dear but departed relative, one can do a better job at predicting what’s next than a bookie at the Churchill Downs Racetrack. MBAs on Wall Street have other methods for predicting the future; namely, chatter at the NYAC or some interactions with folks in the know about an important financial jet blast before ignition.

A happy quack to the Irish Times for running a useful write up. My great uncle would emit a grunt, which is as close as he came to saying, “Good job.”

Stephen E Arnold, September 14, 2017

Search Email: Not Yours. A Competitor’s.

December 2, 2016

I read “This Startup Helps You Deep Snoop Competitor Email Marketing.” I like that “deep snoop” thing. That works pretty well until one loses access to content to analyze. Just ask Geofeedia which is scrambling since it lost access to Twitter and other social media content.

The outfit Rival Explorer offers:

a tool designed to help users improve their email marketing strategy and product pricing and promotion through comprehensive monitoring of their competitor’s email newsletters. After creating a free account, users can browse through a database of marketing emails from over 50,000 brands. Rival Explorer offers access to a number of different email types, including newsletters, cart abandonment emails, welcome emails, and other transactional messages.

In terms of information access, the Rival Explorer customers:

can search by brand, subject, message body, date, day of week, industry, category, and custom tags and keywords. When users select a message, they’re able to view the sender email, subject line, and timestamp of the messages. In addition to those details, users can view the emails as they appear on tablets and smartphones, plus they also can toggle images to get a better idea of design and copy strategy.

You can get more information at this link. Public content and marketing information can be useful it seems.

Stephen E Arnold, December 2, 2016

Distribution Ready Reference

December 16, 2015

Distributions are nifty. Some are easy, like the bell curve. Nice and symmetrical. Others are less regular. If you want to see what type of distribution your data generates, navigate to “Common Probability Distributions: The Data Scientist’s Crib Sheet.” Is it necessary to understand the mathematics underpinning each curve? If you are an MBA, the answer is, “No.” If you are more catholic in your approach, you can use these curves to poke into the underbelly of the numerical recipes. Nice write up. It does not include the Tracy Widom distribution, but the beta distribution may be close enough for MBA horse shoes.

Stephen E Arnold, December 16, 2015

Explaining Markov Chains

March 6, 2015

Do you know what a Markov chain is? If not read about “Markov Chains” on the Circuits of Imagination blog:

“A Markov chain is a set of transitions from one state to the next; Such that the transition from the current state to the next depends only on the current state, the previous and future states do not effect the probability of the transition. A transitions independence from future and past sates is called the Markov property.

This boils down to Markov chains are a way to explain patterns that happen over time and were once used to document human behavior. The chains are not the best way to model human behavior, because they only exist in the present. They do not take into account past or future experiences, otherwise called “memoryless.” The chains can only rely on the action that previously occurred

Markov chains are useful to identify abnormal behavior in systems that don’t exhibit the Markov Property. How? If the system keeps making the wrong decisions based of its program, then it can be diagnosed and repaired. The post explains how the Markov chains are used in coding and provides an example to illustrate how developers can recognize them.

Whitney Grace, March 06, 2015
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Books about Math

January 21, 2015

We’ve run across a list of books that should interest to anyone who would like to understand more about mathematics. These are not textbooks with which to expand our math skills, but rather volumes that take a look at the mathematics field itself. Blogger Kelly J. Rose shares “5 Insanely Great Books About Mathematics You Should Read.” Rose writes:

“I’ve been asked over and over for good books about mathematics for a layperson, someone who hasn’t taken advanced courses in university and is more simply interested in learning about what math is, and some of the more interesting historical figures and results from mathematics. Ironically, when you are a mathematics major at Waterloo, you get the opportunity in 4th year to take a course on the history of mathematics and you get introduced to a few really good books that start to explain the mindset and philosophy behind mathematics and not simply just the theorems and proofs. Here are the 5 books about I most recommend to those who want to understand the mathematical mind and philosophy.”

A few highlights: for a comprehensive history of the field, there’s A History of Mathematics by Carl B. Boyer. For an understanding of what it is like to live the life of a mathematician, it seems Rose cannot recommend The Mathematical Experience by Philip J. David and Reuben Hersh highly enough. Then there’s Proofs and Refutations by Imre Lakatos; Rose says this is likely the most advanced book on his list, yet calls it a quick read. He prescribes it to anyone considering a career in mathematics. Check out the post for more recommendations.

Cynthia Murrell, January 21, 2015

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