Does anyone have advice or general counsel on how to stay fresh in the field? I'm more than 15 years post grad, and I find myself reading fewer papers (no professional license to literature), getting slower at thinking of and understanding stats topics, etc.

That's an interesting question @AdamO. I think you can raise it at Academia SE.

^ So satisfying when a simulation follows a well-characterized distribution.

I think statistics is too big of a field to ever learn, much less 'stay fresh' in. But I recognize that phenomena of being less sharp in myself. Best I've found is to (1) develop a reading habit and (2) have a set of problems that I am interested in.

Also, Sci-Hub is one option for accessing papers... en.wikipedia.org/wiki/Sci-Hub

@Galen Having flashbacks much?

@StephanKolassa Sometimes I get pretty close to forgetting that I was a biochem major in undergrad, but once in a while something (like Dave's comment in that thread) triggers me ⚔️

Fun thread, that one. I will be inflicting some calculus on my offspring soon, and the daughter being interested in life sciences and not too bad in math, I believe it may be time to introduce her to Mr Lotka and Mr Loterra...

@StephanKolassa Haha amazing! Yeah, a little bit of differential equations never hurt anyone 🧮

@StephanKolassa omg. This monstrosity. How can one remember this?!

@User1865345 What, Lotka-Volterra? I find that one actually rather intuitive. Prey increases exponentially, but limited by how often it meets the predator. The predator increases proportionally to how often it meets prey, and otherwise decays exponentially. Not overly realistic, but a very simple introduction to ODE systems. And you get pretty phase plots!

@Galen aced it.

@StephanKolassa no, the one chemical name. But that's an intuitive explanation indeed!

I find that paper about mitochondria energy metabolism a little more intimidating. My wife is a bit of a mitochondriac, so now we have something to talk about around the dinner table.

Actually I got introduced to differential equations in physics than in maths. While I didn't study the vol. 1 of Feynman lectures, it had a nice chapter on linear systems and principle of superposition.

@StephanKolassa good!

Speaking of which, @Galen, if you want to see application of integral equations, check the derivation of asymptotic distribution of Smirnov's goodness of fit statistic. The application is minimal though.

Available in von Mises book. Though I would say it is a sketch only. I have not been through completely yet.

@StephanKolassa okay. Saw it. That's intimidating indeed.

Inspiring: Surrey man graduates from university at the age of 95

Hello to all. I hope that here is the right place for this kind of question.

I am looking for some reference that defines or presents in a clear way the concept of "soft constraints". I understand the concept, but I need a reference to cite in a paper that I an writing.

I currently using a soft constraint in a model, but I need to explain why I using this term, and I think that including a citation is the best way of doing it. But, unfortunately, I did not find any paper or book to cite.

@Galen Reading Cornish-Bowden's Analysis of Enzyme Kinetic Data as an undergraduate was what got me interested in Statistics

(Still have the book, but appear to have mislaid the accompanying floppy disk.)

@Scortchi-ReinstateMonica Right on! Haha some of my older operations research books have accompanying floppy disks :P

@RenatoFernandes Sounds like a question for the main site. You'll want to think about defining what a soft constraint is for the potential answerers.