By Shlens J.

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**Extra info for A tutorial on Principal Component Analysis**

**Example text**

How fast does she have to run her next 10K to “beat” me in terms of percentile ranks? 30 | Chapter 3: Cumulative Distribution Functions Random Numbers CDFs are useful for generating random numbers with a given distribution. Here’s how: • Choose a random probability in the range 0–1. Value to find the value in the distribution that corresponds to the probability you chose. It might not be obvious why this works, but since it is easier to implement than to explain, let’s try it out. Exercise 3-9.

If you consult Wikipedia, you will learn that Thaksin is not the PM of Thailand (at the time I am writing). 01, reflecting the possibility that Wikipedia is wrong. Rules of Probability For frequency probabilities, we can derive rules that relate probabilities of different events. Probably the best known of these rules is: P(A and B) = P(A) P(B) Warning: not always true! where P(A and B) is the probability that events A and B both occur. This formula is easy to remember; the only problem is that it is not always true.

CDFlognormal(x) = CDFnormal(log x) The parameters of the lognormal distribution are usually denoted μ and σ. * In 2008, they interviewed 414,509 respondents and asked about their demographics, health, and health risks. org/wiki/Log-normal_distribution. html. Subsequently I found a paper that proposes the log transform and suggests a cause: Penman and Johnson, “The Changing Shape of the Body Mass Index Distribution Curve in the Population,” Preventing Chronic Disease, 2006 July; 3(3): A74. gov/pmc/ articles/PMC1636707.

### A tutorial on Principal Component Analysis by Shlens J.

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