Category: Statistical computing

Neural nets vs. regression models

Eliot Johnson writes: I have a question concerning papers comparing two broad domains of modeling: neural nets and statistical models. Both terms are catch-alls, within each of which there are, quite obviously, multiple subdomains. For instance, NNs could include ML, DL, AI, and so on. While statistical models should include panel data, time series, hierarchical […]

Maintenance cost is quadratic in the number of features

Bob Carpenter shares this story illustrating the challenges of software maintenance. Here’s Bob: This started with the maintenance of upgrading to the new Boost version 1.69, which is this pull request: https://github.com/stan-dev/math/pull/1082 for this issue: https://github.com/stan-dev/math/issues/1081 The issue happens first, then the pull request, then the fun of debugging starts. Today’s story starts an issue […]

Several post-doc positions in probabilistic programming etc. in Finland

There are several open post-doc positions in Aalto and University of Helsinki in 1. probabilistic programming, 2. simulator-based inference, 3. data-efficient deep learning, 4. privacy preserving and secure methods, 5. interactive AI. All these research programs are connected and collaborating. I (Aki) am the coordinator for the project 1 and contributor in the others. Overall […]

Markov chain Monte Carlo doesn’t “explore the posterior”

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. Sampling to characterize the posterior There’s a misconception among Markov chain Monte Carlo (MCMC) practitioners that the purpose of sampling is to […]

My two talks in Montreal this Friday, 22 Mar

McGill University Biostatistics seminar, Purvis Hall, 102 Pine Ave. West, Room 25, 1-2pm Fri 22 Mar: Resolving the Replication Crisis Using Multilevel Modeling In recent years we have come to learn that many prominent studies in social science and medicine, conducted at leading research institutions, published in top journals, and publicized in respected news outlets, […]

stanc3: rewriting the Stan compiler

I’d like to introduce the stanc3 project, a complete rewrite of the Stan 2 compiler in OCaml. Join us! With this rewrite and migration to OCaml, there’s a great opportunity to join us on the ground floor of a new era. Your enthusiasm for or expertise in programming language theory and compiler development can help […]

HMC step size: How does it scale with dimension?

A bunch of us were arguing about how the Hamiltonian Monte Carlo step size should scale with dimension, and so Bob did the Bob thing and just ran an experiment on the computer to figure it out. Bob writes: This is for standard normal independent in all dimensions. Note the log scale on the x […]

R fixed its default histogram bin width!

I remember hist() in R as having horrible defaults, with the histogram bars way too wide. (See this discussion: A key benefit of a histogram is that, as a plot of raw data, it contains the seeds of its own error assessment. Or, to put it another way, the jaggedness of a slightly undersmoothed histogram […]

Should he go to grad school in statistics or computer science?

Someone named Nathan writes: I am an undergraduate student in statistics and a reader of your blog. One thing that you’ve been on about over the past year is the difficulty of executing hypothesis testing correctly, and an apparent desire to see researchers move away from that paradigm. One thing I see you mention several […]

NYC Meetup Thursday: Under the hood: Stan’s library, language, and algorithms

I (Bob, not Andrew!) will be doing a meetup talk next Thursday in New York City. Here’s the link with registration and location and time details (summary: pizza unboxing at 6:30 pm in SoHo): Bayesian Data Analysis Meetup: Under the hood: Stan’s library, language, and algorithms After summarizing what Stan does, this talk will focus […]

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Reproducibility and Stan

Aki prepared these slides which cover a series of topics, starting with notebooks, open code, and reproducibility of code in R and Stan; then simulation-based calibration of algorithms; then model averaging and prediction. Lots to think about here: t…

Reproducibility and Stan

Aki prepared these slides which cover a series of topics, starting with notebooks, open code, and reproducibility of code in R and Stan; then simulation-based calibration of algorithms; then model averaging and prediction. Lots to think about here: t…

“My advisor and I disagree on how we should carry out repeated cross-validation. We would love to have a third expert opinion…”

Youyou Wu writes: I’m a postdoc studying scientific reproducibility. I have a machine learning question that I desperately need your help with. My advisor and I disagree on how we should carry out repeated cross-validation. We would love to have a third expert opinion… I’m trying to predict whether a study can be successfully replicated […]

The post “My advisor and I disagree on how we should carry out repeated cross-validation. We would love to have a third expert opinion…” appeared first on Statistical Modeling, Causal Inference, and Social Science.