Hi Saang-Yoon,<div>I think some models are just poorly behaved and no improved algorithm will solve their convergence problems. You might look at reparameterizing to avoid some of the highest correlations, or fixing some of the parameters that are the most autocorrelated.</div>
<div><br></div><div>If you want to try additional MCMC options in ADMB, a few to consider are -mcgrope to fatten the tails of the jump function, -mcrb to reduce correlations in the jump, and -hybrid (along with inputs for <span style="background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial,Helvetica,sans-serif;font-size:13px">-hyeps and -hynsteps </span>to use an alternative algorithm). You can read a little discussion about these options here:
<a href="https://groups.google.com/forum/#!topic/admb-users/QZhF0CaJLnw">https://groups.google.com/forum/#!topic/admb-users/QZhF0CaJLnw</a>. -mcgrope and -mcrb are also described (a little) in the user manual.</div><div>
-Ian</div><div><br><div class="gmail_quote">On Fri, May 4, 2012 at 10:08 AM, Saang-Yoon <span dir="ltr"><<a href="mailto:shyunuw@gmail.com" target="_blank">shyunuw@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi, all.<br>
>From my TPL and DAT files, the estimates (i.e., MLE) and its SE are<br>
reasonable. However, Bayesian samples from the same codes are not<br>
satisfactory in terms of diagnosis. I ran large MCMC runs (e.g., ><br>
20 million runs), thinning samples at an enough interval (e.g., every<br>
5000 runs). Despite that, the posterior samples are not<br>
satisfactory. I wonder what kind of additional commands I should<br>
try. I used only the following commands: (i) -mcmc; (ii) -mcsave,<br>
and (iii) -mceval. Thank you.<br>
<span class="HOEnZb"><font color="#888888">Saang-Yoon<br>
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