Bayesian Histogram Analysis - Explained by Phil Godwin

978 views
893 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
978
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Bayesian Histogram Analysis - Explained by Phil Godwin

  1. 1. Naïve Bayesian Histogram Analysis? by Phil Godwin, Vice President of Sales, Clear TechnologiesI often remind my wife that I am the Despite being armed with both the ‘it’s“worst buyer” because of my indignation the secret sauce’ and the more technicalfor “being sold.” Most people find this to definition, I cringe each time I say thebe especially surprising since I relish and phrase. It reminds me of the fact that I doenjoy the art of selling. not fully understand its meaning and cannot explain it in layman’s terms.Over the past 20 years, my exasperationwith salespeople has grown. During that Do I really despise all salespeople? Intime, I have bought from, been speaking with a colleague, I discoveredemployed amongst, and employed the best, layman explanation. Quitenumerous salespeople. One could almost simply, Naïve Bayesian Histogram Analysisconclude that I have become utilizes the same ‘profiling’ process I use todisenchanted with salespeople simply draw my conclusion about certainbecause of their title. However, upon salespeople.further introspection, I have come tounderstand I do not despise all ‘Histogram’ in the phrase, ‘Naïvesalespeople, but rather salespeople that Bayesian Histogram Analysis,’ simplydo certain things, some of which include: means frequency, and ‘Bayesian’selling something with which they are not algorithm is simply a method by whichfully versed, pushing their product without parallels can be drawn. In my example,fully understanding my problem, the histogram portion is that I havecomparing my problem to one that does become suspicious of salespeople basednot apply to my situation, or using on the number of times I have hadundefined big, vague phrases to seem negative experiences with individualscredible. One such big, vague phrase is: who have the title of ‘salesperson’. And,Naïve Bayesian Histogram Analysis in my salesperson example, the Bayesianalgorithm. algorithm portion is that I have become outright annoyed by salespeople whoNaïve Bayesian Histogram Analysis? My exhibit certain behavior.technology specialist tells me that theNaïve Bayesian Histogram Analysis So what makes it better than me?algorithm is the ‘secret sauce’ that runs Obviously, one should not utilize aour premier security product, Dynamic software algorithm to determine hisLog Analysis™. He further explains that impression of a salesperson. However,Naïve Bayesian Histogram Analysis is when placed in a security context, whatimportant because it uniquely makes Naïve Bayesian Histogram Analysis"fingerprints" known security and better - than our own years of experienceperformance issues, while establishing a or the time we spend analyzing andbaseline for positive or neutrally categorizing those experiences - is that itacceptable network traffic utilizing quickly and efficiently infers patterns tostandard deviation. help make accurate security and network decisions. About Clear Technologies. Since 1993, Clear’s customers have relied on them to meet their hardware needs. Today, their customers look to them to increase their organizational effectiveness by providing continuity, infrastructure, security, and virtualization solutions. Based in Coppell, Texas, Phil can be reached at www.cleartechnologies.net/DynamicLogAnalysis or (972) 906 -7500 or pgodwin@cleartechnologies.net.

×