Hello


#1

Hello


#2

You have discrete event simulation in Kendall’s notation a M/G/3 model. Since you have a memory-less arrival process, 3 servers and the service time is not exponentially distributed. In R you “need” a priority queue for the events, rpois and runif for the events and service times. You sample an event and put it into the queue.
I don’t see what it has to do with Stan? There is a way with Stochastic Differential Equations. Thus if
you transform the problem, it might be put into Stan ODE solver.


#4

Seriously, dude?
http://lmgtfy.com/?q=how+to+get+help+with+homework+online+for+free


#5

More seriously, who’s teaching a class with Stan that we don’t know about?


#6

on that theme, if there are any teaching materials anyone could recommend? I’m trying to get my team up to speed on Stan but it’s slow going since I have to prep slides and examples myself.


#7

What level are you looking for?


#8

I guess that’s a good question. Competent modelers but unfamiliar with Bayesian approaches. In other words, they can optimize a model with SciPy or MATLAB, and now want to compute posterior distributions.


#9

If you get past the preliminaries I really like the case studies. Specifically I think some of Bob’s case studies do a really good job of working with derived quantities and that demonstrates the advantages of Bayesian inference really well.


#10

There are some books and youtube videos (linked on the main website). The Stan team also give paid short courses that can be tailored for a particular audience if that’s an option for you. Contact Bob or Mike or Andrew for information about that.