Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Bob Carpenter
Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England
Loading metrics Open Access Peer-reviewed Research Article ? This is an uncorrected proof. Citation: Ward T, Morris M, Gelman A, Carpenter B, Ferguson W, Overton C, et al. (2023) Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England. PLoS Comput Biol 19(11): e1011580. https://doi.org/10.1371/journal.pcbi.1011580 Editor: Joseph T.
Replacement dynamics and the pathogenesis of the Alpha, Delta and Omicron variants of SARS-CoV-2 | Epidemiology & Infection | Cambridge Core
Introduction New SARS-CoV-2 variants causing COVID-19 are a major concern to public health worldwide, due to the potential for increases in incidence and infection severity. Understanding the characteristics of these new variants is critical, to inform the assessment of risk.
MCMC for big datasets: faster sampling with JAX and the GPU
MCMC for big datasets: faster sampling with JAX and the GPU Scaling PyMC using JAX to sample on the GPU AUTHORED BY Martin Ingram DATE 2021-12-22 This post was written by Martin Ingram, with help from Thomas Wiecki and the PyMC team. You can find the original version on his blog. You'll often hear people say that MCMC is too slow for big datasets. For the very biggest datasets with millions of observations, there may be some truth to that.
Markov chain Monte Carlo doesn't "explore the posterior"
(This article was first published on R – Statistical Modeling, Causal Inference, and Social Science, and kindly contributed to R-bloggers) First some background, then the bad news, and finally the good news. Spoiler alert: The bad news is that exploring the posterior is intractable; the good news is that we don’t need to explore all of it.
Developing Firefighter Resiliency
Three Key Messages– This is important. You can do it. I won't give up on you. Training ordinary people to do extraordinary things requires an understanding of how we learn. Developing Firefighter Resiliency starts with the basic psychophysical aspects of learning. The fire service has unwittingly used a failure-based training model for many years. Hands-on training exercises are often based on unachievable objectives.
StanCon Helsinki streaming live now (and tomorrow)
StanCon Helsinki streaming live now (and tomorrow) We’re streaming live right now! Timezone is Eastern European Summer Time (EEST) +0300 UTC Here’s a link to the full program. There have already been some great talks and they’ll all be posted with slides and runnable source code after the conference on the Stan web site. The post StanCon Helsinki streaming live now (and tomorrow) appeared first on Statistical Modeling, Causal Inference, and Social Science.
Thanks, NVIDIA
Andrew and I both received a note like this from NVIDIA: In case other people are interested, NVIDA’s GPU grant program provides ways for faculty or research scientists to request GPUs; they also have graduate fellowships and larger programs. The pull requests are stacked up and being reviewed and integrated into the testing framework as I write this.
Where do I learn about log_sum_exp, log1p, lccdf, and other numerical analysis tricks?
I was helping a colleague recently fix his MATLAB code by using log_sum_exp and log1m tricks. The natural question he had was, “where do you learn this stuff?” I checked Numerical Recipes, but the statistical parts are actually pretty thin (at least in my 1994 edition). Do you know of any books/papers that describe these techniques? I’d love to hear this blog’s answers to these questions. I replied that I learned numerical analysis “on the street” through HMM implementations.
Three new domain-specific (embedded) languages with a Stan backend
One is an accident. Two is a coincidence. Three is a pattern. Perhaps it’s no coincidence that there are three new interfaces that use Stan’s C++ implementation of adaptive Hamiltonian Monte Carlo (currently an updated version of the no-U-turn sampler). These are in contrast with systems that entirely reimplement a version of the no-U-turn sampler, such as PyMC3, ADMB, and NONMEM.
Stan Roundup, 10 November 2017
Stan Roundup, 10 November 2017 We’re in the heart of the academic season and there’s a lot going on. James Ramsey reported a critical performance regression bug in Stan 2.17 (this affects the latest CmdStan and PyStan, not the latest RStan). Sean Talts and Daniel Lee diagnosed the underlying problem as being with the change from char* to std::string arguments—you can’t pass char* and rely on the implicit std::string constructor without the penalty of memory allocation and copying.
Stan Roundup, 27 October 2017
I missed two weeks and haven’t had time to create a dedicated blog for Stan yet, so we’re still here. This is only the update for this week. From now on, I’m going to try to concentrate on things that are done, not just in progress so you can get a better feel for the pace of things getting done. Not one, but two new devs! This is my favorite news to post, hence the exclamation. Matthijs Vákár from University of Oxford joined the dev team.
Halifax, NS, Stan talk and course Thu 19 Oct
Halfiax, here we come. I (Bob, not Andrew) am going to be giving a talk on Stan and then Mitzi and I will be teaching a course on Stan after that. The public is invited, though space is limited for the course. Here are details if you happen to be in the Maritime provinces.
Stan Biweekly Roundup, 6 October 2017
Stan Biweekly Roundup, 6 October 2017 I missed last week and almost forgot to add this week’s. Jonah Gabry returned from teaching a one-week course for a special EU research institute in Spain. Mitzi Morris has been knocking out bug fixes for the parser and some pull requests to refactor the underlying type inference to clear the way for tuples, sparse matrices, and higher-order functions.
Will Stanton hit 61 home runs this season?
[edit: Juho Kokkala corrected my homework. Thanks! I updated the post. Also see some further elaboration in my reply to Andrew’s comment. As Andrew likes to say …] So far, Giancarlo Stanton has hit 56 home runs in 555 at bats over 149 games. Miami has 10 games left to play. What’s the chance he’ll hit 61 or more home runs? Let’s make a simple back-of-the-envelope Bayesian model and see what the posterior event probability estimate is.
Batting Averages: Bayesian vs. MLE Estimates
It’s American baseball season, and I’ve been itching to do more baseball stats. So in the interest of explaining Bayesian posterior inference with an example… Batting Averages A player’s batting average is his number of “hits” divided by his number of “at bats“. Binomial Model of Batting For simplicitly, we’ll model a sequence of at bats using a binomial distribution with a probability of getting a hit in each at bat.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Bob
Contact Bob, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack