<div dir="ltr"><div>Thanks Mattias,</div><div><br></div><div>Estimation of model parameters is an important topic. You will find that most of us in the list, had to deal with such issues. </div><div><br></div><div>It is commendable that you decided to share code - model reproducibility is important. You may want to follow the discussion on the data and model sharing group they have interesting ways of coding differential equation models that help sharing those. This may be helpful in the future, </div><div><br></div><div><div>Glancing at the paper, I see you took an approach of simplifying the problem to allow solution. Many of us use assumptions. In fact, the model structure we use is an assumption as well. So there is a level of human judgment embedded in the estimation method. Although in your case it is small.</div></div><div><br></div><div>I am curious how other people in the list estimate model parameters. I encountered several approaches in the past that build a spectrum of estimation techniques that can be characterized by amount of human decision making.</div><div><br></div><div>One interesting end of the spectrum was reported by Steve Leff, where human experts go thorough a controlled process to determine parameters. Steve may want to elaborate on this technique. And I also know he used other techniques in the past as well from other sides of this estimation spectrum.</div><div><br></div><div>In the middle of the spectrum some modelers just picking numbers reported in the literature to drive models. </div><div><br></div><div>On the other extreme spectrum end, there are robust mathematical estimation techniques that rely on computation once the model is defined. The paper Matthias attached falls near this end of the spectrum.</div><div><br></div><div>Where do modelers in this group find themselves? Do you rely more on human judgment for estimation? Or do you find yourself on the end of relying on numerical techniques?</div><div><br></div><div>I hope people choose to share experiences and insights.</div><div><br></div><div> Jacob</div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Sep 30, 2015 at 9:42 AM, Matthias Chung <span dir="ltr"><<a href="mailto:mcchung@vt.edu" target="_blank">mcchung@vt.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="-ms-word-wrap: break-word;">Hi everyone,<div><br></div><div>Some of you might be interested in parameter estimation in population dynamic models. We developed a new efficient and robust estimation code in Matlab to “match” data with a differential equation model. We provide the code here:</div><div><br></div><div>http://<a href="http://www.math.vt.edu/people/mcchung/resources/Continuous_Shooting.zip" target="_blank">www.math.vt.edu/people/mcchung/resources/Continuous_Shooting.zip</a>. </div><div><br></div><div>The associated preprint can be found here:</div><div><a href="http://arxiv.org/abs/1509.06926" target="_blank">http://arxiv.org/abs/1509.06926</a></div><div><br></div><div>Thanks,</div><div><br></div><div>Matthias Chung</div><br><div>
------<br>Matthias Chung<br><br>Department of Mathematics, Virginia Tech<br>McBryde, RM 474<br>225 Stanger Street<br>Blacksburg, VA 24061<br><br>Phone: +1 (540) 231-3446<br>Fax : +1 (540) 231-5960<br><a href="http://www.math.vt.edu/people/mcchung" target="_blank">www.math.vt.edu/people/mcchung</a>
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