On to find out about analytic solutions to Kramers' unfolding rates.
-Update: I found a better [reference][] while writing my [[sawsim]]
-paper, listing the mean and standard deviation of the Gumbel
-distribution. So many names... Anyway, the `pysawsim` tests now use
-the [improved guessing procedure][].
+Update: I figured out how to use the [NIST reference][] while writing
+my [[sawsim]] paper, listing the mean and standard deviation of the
+Gumbel distribution. So many names... Anyway, the `pysawsim` tests
+now use the [improved guessing procedure][].
-[reference]: http://www.itl.nist.gov/div898/handbook/eda/section3/eda366g.htm
+[NIST reference]: http://www.itl.nist.gov/div898/handbook/eda/section3/eda366g.htm
[improved guessing procedure]: http://www.physics.drexel.edu/~wking/code/git/gitweb.cgi?p=sawsim.git;a=blob;f=pysawsim/test/bell_rate.py;hb=837c425eaeccae280cc7f7784d03dfcfcb03678c#l106
[[!tag tags/sawsim]]
There is a nice discussion of aging in general, but not much math [here](http://longevity-science.org/Failure-Models-2006.pdf).
+Update: Related posts:
+
+* [[Gompertz-Gumbel distributions|Gompertz-Gumbel_distributions]]
+* [[Gompertz paper'|Gompertz_paper]]
+* [[Giving up on Gompertz theory]]
+
[[!tag tags/theory]]
[[!tag tags/sawsim]]