Tag Archives: Bayesian
MCMC in Python: Statistical model stuck on a stochastic system dynamics model in PyMC
My recent tutorial on how to stick a statistical model on a systems dynamics model in PyMC generated a good amount of reader interest, as well as an interesting comment from Anand Patil, who writes: Something that might interest you … Continue reading
Filed under global health, MCMC, statistics
MCMC in Python: How to stick a statistical model on a system dynamics model in PyMC
A recent question on the PyMC mailing list inspired me. How can you estimate transition parameters in a compartmental model? I did a lit search for just this when I started up my generic disease modeling project two years ago. … Continue reading
Filed under global health, MCMC, statistics
Child Mortality Paper
Check it out, my first published research in global health: Neonatal, postneonatal, childhood, and under-5 mortality for 187 countries, 1970—2010: a systematic analysis of progress towards Millennium Development Goal 4. I’m the ‘t’ in et al, and my contribution was … Continue reading
Filed under global health
Multilevel (hierarchical) modeling: what it can and cannot do in Python
I re-read a short paper of Andrew Gelman’s yesterday about multilevel modeling, and thought “That would make a nice example for PyMC”. The paper is “Multilevel (hierarchical) modeling: what it can and cannot do, and R code for it is … Continue reading
Filed under MCMC, statistics
MCMC in Python: PyMC for Bayesian Model Selection
(Updated 9/2/2009, but still unfinished; see other’s work on this that I’ve collected) I never took a statistics class, so I only know the kind of statistics you learn on the street. But now that I’m in global health research, … Continue reading
Filed under MCMC, statistics
MCMC in Python: PyMC for Bayesian Probability
I’ve got an urge to write another introductory tutorial for the Python MCMC package PyMC. This time, I say enough to the comfortable realm of Markov Chains for their own sake. In this tutorial, I’ll test the waters of Bayesian … Continue reading
Filed under MCMC, probability
