Posts Tagged ‘ random numbers ’

Pseudo-Random vs. Random Numbers in R

November 25, 2011
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Pseudo-Random vs. Random Numbers in R

Happy Thanksgiving, everyone. Earlier today, I found an interesting post from Bo Allen on pseudo-random vs random numbers, where the author uses a simple bitmap (heat map) to show that the rand function in PHP has a systematic pattern and compares these to truly random numbers obtained from random.org. The post’s results suggest that pseudo-randomness in PHP is

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Character occurrence in passwords

June 16, 2011
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Character occurrence in passwords

As everyone knows, it seems that Sony is taking a bit of a battering from hackers.  Thanks to Sony, numerous account and password details are now circulating on the internet. Recently, Troy Hunt carried out a brief analysis of the password structure. Here is a summary of his post: There were around 40,000 passwords, of which

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Example 8.35: Grab true (not pseudo) random numbers; passing API URLs to functions or macros

April 19, 2011
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Example 8.35: Grab true (not pseudo) random numbers; passing API URLs to functions or macros

Usually, we're content to use a pseudo-random number generator. But sometimes we may want numbers that are actually random-- an example might be for randomizing treatment status in a randomized controlled trial.The site Random.org provides truly rando...

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Random variable generation (Pt 3 of 3)

January 12, 2011
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Random variable generation (Pt 3 of 3)

Ratio-of-uniforms This post is based on chapter 1.4.3 of Advanced Markov Chain Monte Carlo.  Previous posts on this book can be found via the  AMCMC tag. The ratio-of-uniforms was initially developed by Kinderman and Monahan (1977) and can be used for generating random numbers from many standard distributions. Essentially we transform the random variable of

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Random variable generation (Pt 2 of 3)

December 2, 2010
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Random variable generation (Pt 2 of 3)

Acceptance-rejection methods This post is based on chapter 1.4 of Advanced Markov Chain Monte Carlo. Another method of generating random variates from distributions is to use acceptance-rejection methods. Basically to generate a random number from , we generate a RN from an envelope distribution , where .  The acceptance-rejection algorithm is as follows: Repeat until

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Random variable generation (Pt 1 of 3)

November 28, 2010
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Random variable generation (Pt 1 of 3)

As I mentioned in a recent post, I’ve just received a copy of Advanced Markov Chain Monte Carlo Methods. Chapter 1.4 in the book (very quickly) covers random variable generation. Inverse CDF Method A standard algorithm for generating random numbers is the inverse cdf method. The continuous version of the algorithm is as follows: 1.

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