Intro to Probability
January 23, 2013
For anyone whose basic understanding of probability theory is incomplete, Math ? Programming kindly offers a thorough introduction in “Probability Theory—A Primer.” Blogger Jeremy Kun notes that key concepts in artificial intelligence, machine learning, and statistics are based upon probability theory. Referring to plans for his blog, he goes on:
“A number of our future posts will rely on the ideas and terminology we lay out in this post. Our first formal theory of machine learning will be deeply ingrained in probability theory, we will derive and analyze probabilistic learning algorithms, and our entire treatment of mathematical finance will be framed in terms of random variables.”
Kun simplifies by framing his explanation finitely in terms of naive set theory and without the complications of measure theory. He emphasizes:
“This primer is not meant to connect probability theory to the real world. Indeed, to do so would be decidedly unmathematical. We are primarily concerned with the mathematical formalisms involved in the theory of probability, and we will leave the philosophical concerns and applications to future posts. The point of this primer is simply to lay down the terminology and basic results needed to discuss such topics to begin with.”
The lesson goes on in depth, covering finite probability spaces, random variables, expected value, and variance and covariance. Kun throws in plenty of helpful definitions and formulas along the way. This might be one to review now and tuck away for future reference.
Cynthia Murrell, January 23, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
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