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I was reading this question about the algorithm that R uses to sample from the Exponential($\lambda$) distribution. It looks like R uses the Ahrens-Dieter algorithm to sample from the exponential distribution. The reference for it is a paper from 1972 that makes a surprising claim that this algorithm is a little faster than just delivering $\frac{-\ln(u)}{\lambda}$ (where U~Uniform(0,1); as the Inverse Transform would have us do).

I find this very surprising. First of all, I am not sure the results from 1972 carry much weight in 2022. Secondly, why the need for an algorithm in the first place as opposed to just delivering $\frac{-\ln(u)}{\lambda}$ (as the Inverse Transform would have us do)? How slow can calculating $\frac{-\ln(u)}{\lambda}$ be? Maybe it was slow in 1972. Or maybe there is another reason?

I was reading this question about the algorithm that R uses to sample from the Exponential($\lambda$) distribution. It looks like R uses the Ahrens-Dieter algorithm to sample from the exponential distribution. The reference for it is a paper from 1972 that makes a surprising claim that this algorithm is a little faster (in terms of microseconds, not in terms of Big O computational cycles) than just delivering $\frac{-\ln(u)}{\lambda}$ (where U~Uniform(0,1); as the Inverse Transform would have us do).

I find this very surprising. First of all, I am not sure the results from 1972 carry much weight in 2022. Secondly, why the need for an algorithm in the first place as opposed to just delivering $\frac{-\ln(u)}{\lambda}$ (as the Inverse Transform would have us do)? How slow can calculating $\frac{-\ln(u)}{\lambda}$ be? Maybe it was slow in 1972. Or maybe there is another reason?

I was reading this question about the algorithm that R uses to sample from the Exponential($\lambda$) distribution. It looks like R uses the Ahrens-Dieter algorithm to sample from the exponential distribution. The reference for it is a paper from 1972 that makes a surprising claim that this algorithm is a little faster than just delivering $\frac{-ln(u)}{\lambda}$ (where U~Uniform(0,1); as the Inverse Transform would have us do). I find this very surprising. First of all, I am not sure the results from 1972 carry much weight in 2022. Secondly, why the need for an algorithm in the first place as opposed to just delivering $\frac{-ln(u)}{\lambda}$ (as the Inverse Transform would have us do)? How slow can calculating $\frac{-ln(u)}{\lambda}$ be? Maybe it was slow in 1972. Or maybe there is another reason?

I was reading this question about the algorithm that R uses to sample from the Exponential($\lambda$) distribution. It looks like R uses the Ahrens-Dieter algorithm to sample from the exponential distribution. The reference for it is a paper from 1972 that makes a surprising claim that this algorithm is a little faster than just delivering $\frac{-\ln(u)}{\lambda}$ (where U~Uniform(0,1); as the Inverse Transform would have us do). 

I find this very surprising. First of all, I am not sure the results from 1972 carry much weight in 2022. Secondly, why the need for an algorithm in the first place as opposed to just delivering $\frac{-\ln(u)}{\lambda}$ (as the Inverse Transform would have us do)? How slow can calculating $\frac{-\ln(u)}{\lambda}$ be? Maybe it was slow in 1972. Or maybe there is another reason?

I was reading this question about the algorithm that R uses to sample from the Exponential($\lambda$) distribution. It looks like R uses the Ahrens-Dieter algorithm to sample from the exponential distribution. The reference for it is a paper from 1972 that makes a surprising claim that this algorithm is a little faster than just delivering $\frac{-ln(u)}{\lambda}$ (where U~Uniform[0,1]; as the Inverse Transform would have us do). I find this very surprising. First of all, I am not sure the results from 1972 carry much weight in 2022. Secondly, why the need for an algorithm in the first place as opposed to just delivering $\frac{-ln(u)}{\lambda}$ (as the Inverse Transform would have us do)? How slow can calculating $\frac{-ln(u)}{\lambda}$ be? Maybe it was slow in 1972. Or maybe there is another reason?

I was reading this question about the algorithm that R uses to sample from the Exponential($\lambda$) distribution. It looks like R uses the Ahrens-Dieter algorithm to sample from the exponential distribution. The reference for it is a paper from 1972 that makes a surprising claim that this algorithm is a little faster than just delivering $\frac{-ln(u)}{\lambda}$ (where U~Uniform(0,1); as the Inverse Transform would have us do). I find this very surprising. First of all, I am not sure the results from 1972 carry much weight in 2022. Secondly, why the need for an algorithm in the first place as opposed to just delivering $\frac{-ln(u)}{\lambda}$ (as the Inverse Transform would have us do)? How slow can calculating $\frac{-ln(u)}{\lambda}$ be? Maybe it was slow in 1972. Or maybe there is another reason?