Posts Tagged ‘ Statistical computing ’

We’re hiring! hiring! hiring! hiring!

January 17, 2017
By

[insert picture of adorable cat entwined with Stan logo] We’re hiring postdocs to do Bayesian inference. We’re hiring programmers for Stan. We’re hiring a project manager. How many people we hire depends on what gets funded. But we’re hiring a few people for sure. We want the best best people who love to collaborate, who […] The post We’re hiring! hiring! hiring! hiring! appeared first on Statistical Modeling, Causal Inference,…

Read more »

Stan JSS paper out: “Stan: A probabilistic programming language”

January 13, 2017
By
Stan JSS paper out:  “Stan: A probabilistic programming language”

As a surprise welcome to 2017, our paper on how the Stan language works along with an overview of how the MCMC and optimization algorithms work hit the stands this week. Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. Stan: […] The post Stan JSS paper out: “Stan: A probabilistic programming language” appeared first on…

Read more »

“A Conceptual Introduction to Hamiltonian Monte Carlo”

January 12, 2017
By
“A Conceptual Introduction to Hamiltonian Monte Carlo”

Michael Betancourt writes: Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous understanding of why it performs so well on difficult problems and how it is best applied in practice. Unfortunately, that understanding is con- fined within the mathematics of differential geometry which has limited […] The post “A Conceptual Introduction to Hamiltonian Monte Carlo” appeared first on Statistical Modeling,…

Read more »

Michael found the bug in Stan’s new sampler

December 22, 2016
By
Michael found the bug in Stan’s new sampler

Gotcha! Michael found the bug! That was a lot of effort, during which time he produced ten pages of dense LaTeX to help Daniel and me understand the algorithm enough to help debug (we’re trying to write a bunch of these algorithmic details up for a more general audience, so stay tuned). So what was […] The post Michael found the bug in Stan’s new sampler appeared first on Statistical…

Read more »

“The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling”

December 10, 2016
By

Here’s Michael Betancourt writing in 2015: Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however, the algorithm may be too expensive to implement, leaving us to consider the utility of approximations such as […] The post “The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling”…

Read more »

Some U.S. demographic data at zipcode level conveniently in R

December 2, 2016
By

Ari Lamstein writes: I chuckled when I read your recent “R Sucks” post. Some of the comments were a bit … heated … so I thought to send you an email instead. I agree with your point that some of the datasets in R are not particularly relevant. The way that I’ve addressed that is […] The post Some U.S. demographic data at zipcode level conveniently in R appeared first…

Read more »

Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature of consciousness

November 21, 2016
By
Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature of consciousness

Bayesian data analysis, as my colleagues and I have formulated it, has a human in the loop. Here’s how we put it on the very first page of our book: The process of Bayesian data analysis can be idealized by dividing it into the following three steps: 1. Setting up a full probability model—a joint […] The post Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature…

Read more »

Only on the internet . . .

November 17, 2016
By

I had this bizarrely escalating email exchange. It started with this completely reasonable message: Professor, I was unable to run your code here: https://www.r-bloggers.com/downloading-option-chain-data-from-google-finance-in-r-an-update/ Besides a small typo [you have a 1 after names (options)], the code fails when you actually run the function. The error I get is a lexical error: Error: lexical error: […] The post Only on the internet . . . appeared first on Statistical Modeling,…

Read more »

Kaggle Kernels

November 15, 2016
By

Anthony Goldbloom writes: In late August, Kaggle launched an open data platform where data scientists can share data sets. In the first few months, our members have shared over 300 data sets on topics ranging from election polls to EEG brainwave data. It’s only a few months old, but it’s already a rich repository for […] The post Kaggle Kernels appeared first on Statistical Modeling, Causal Inference, and Social Science.

Read more »

Stan Webinar, Stan Classes, and StanCon

November 14, 2016
By

This post is by Eric. We have a number of Stan related events in the pipeline. On 22 Nov, Ben Goodrich and I will be holding a free webinar called Introduction to Bayesian Computation Using the rstanarm R Package. Here is the abstract: The goal of the rstanarm package is to make it easier to use Bayesian […] The post Stan Webinar, Stan Classes, and StanCon appeared first on Statistical Modeling, Causal…

Read more »


Subscribe

Email:

  Subscribe