# Number of iterations

**URL:** <https://discourse.mc-stan.org/t/number-of-iterations/1674>\
**Category:** General\
**Created:** [August 24, 2017, 5:35pm UTC](https://discourse.mc-stan.org/t/number-of-iterations/1674 "2017-08-24T17:35:32Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![Bob\_Carpenter](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bob_carpenter/32/9230_2.png) [@Bob\_Carpenter](https://discourse.mc-stan.org/u/Bob_Carpenter)\
**Post date:** [August 28, 2017, 8:35pm UTC](https://discourse.mc-stan.org/t/number-of-iterations/1674/3 "2017-08-28T20:35:46Z")

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> [@sakrejda](#):
>
> Specifically for the Stan implementation of HMC you can expect for most well-parameterized models to get more than one effective sample per 10 iterations.

This varies tremdously with the geometry of the problem. What you shoudl be seeing is that if you run it for twice as many iterations, you get twice as large an `n_eff`. It may be mixing slowly, but that will show you if it’s mixing.

> [@mmorenozam](#):
>
> In the mentioned post, someone asked if it is possible restart a new simulation with the output of a previous simulation and the answer by that time was that wasn’t possible, it is now?

In the works for Stan 3. Mitzi coded up the basic I/O, but we’re waiting on refactoring some of the interface code so we don’t have to code this all up twice.

> [@sakrejda](#):
>
> All that stuff about running a million iterations and thinning by 10k is irrelevant for Stan/HMC, don’t do that.

This is only going to matter if (a) you have really long autocorrelation times in an otherwise well-behaved model, and (b) you don’t have enough memory. You always lose information by thinning.

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