# Posts Tagged ‘ AMCMC ’

## 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

## 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

## 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.

## Advanced Markov Chain Monte Carlo Methods (AMCMC)

November 27, 2010
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I’ve just received my copy of Advanced Markov Chain Monte Carlo Methods, by Liang, Liu, & Carroll. Although my PhD didn’t really involve any Bayesian methodology (and my undergrad was devoid of any Bayesian influence), I’ve found that the sort of problems I’m now tackling in systems biology demand a Bayesian/MCMC approach. There are a