Gronau and Wagemakers write: The bridgesampling package facilitates the computation of the marginal likelihood for a wide range of different statistical models. For models implemented in Stan (such that the constants are retained), executing the code bridge_sampler(stanfit) automatically produces an estimate of the marginal likelihood. Full story is at the link.

**Statistical computing**category.

## In the open-source software world, bug reports are welcome. In the science publication world, bug reports are resisted, opposed, buried.

Mark Tuttle writes: If/when the spirit moves you, you should contrast the success of the open software movement with the challenge of published research. In the former case, discovery of bugs, or of better ways of doing things, is almost always WELCOMED. In some cases, submitters of bug reports, patches, suggestions, etc. get “merit badges” […]

## The network of models and Bayesian workflow

This is important, it’s been something I’ve been thinking about for decades, it just came up in an email I wrote, and it’s refreshingly unrelated to recent topics of blog discussion, so I decided to just post it right now out of sequence (next slot on the queue is in May 2018). Right now, standard […]

## Workshop on Interpretable Machine Learning

Andrew Gordon Wilson sends along this conference announcement: NIPS 2017 Symposium Interpretable Machine Learning Long Beach, California, USA December 7, 2017 Call for Papers: We invite researchers to submit their recent work on interpretable machine learning from a wide range of approaches, including (1) methods that are designed to be more interpretable from the start, […]

## Tenure-Track or Tenured Prof. in Machine Learning in Aalto, Finland

This job advertisement for a position in Aalto, Finland, is by Aki We are looking for a professor to either further strengthen our strong research fields, with keywords including statistical machine learning, probabilistic modelling, Bayesian inference, kernel methods, computational statistics, or complementing them with deep learning. Collaboration with other fields is welcome, with local opportunities […]

## “5 minutes? Really?”

Bob writes: Daniel says this issue https://github.com/stan-dev/stan/issues/795#issuecomment-26390557117 is an easy 5-minute fix. In my ongoing role as wet blanket, let’s be realistic. It’s sort of like saying it’s an hour from here to Detroit because that’s how long the plane’s in the air. Nothing is a 5 minute fix (door to door) for Stan and […]

## “From ‘What If?’ To ‘What Next?’ : Causal Inference and Machine Learning for Intelligent Decision Making”

Panos Toulis writes in to announce this conference: NIPS 2017 Workshop on Causal Inference and Machine Learning (WhatIF2017) “From ‘What If?’ To ‘What Next?’ : Causal Inference and Machine Learning for Intelligent Decision Making” — December 8th 2017, Long Beach, USA. Submission deadline for abstracts and papers: October 31, 2017 Acceptance decisions: November 7, 2017 […]

## 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 […]

## Call for papers: Probabilistic Programming Languages, Semantics, and Systems (PPS 2018)

I’m on the program committee and they say they’re looking to broaden their horizons this year to include systems like Stan. The workshop is part of POPL, the big programming language theory conference. Here’s the official link PPS 2018 home page Call for extended abstracts (2 pages) The submissions are two-page extended abstracts and the […]

## The fundamental abstractions underlying BUGS and Stan as probabilistic programming languages

Probabilistic programming languages I think of BUGS and Stan as probabilistic programming languages because their variables may be used to denote random variables, with function application doing the right thing in terms of propagating randomness (usually encoding uncertainty in a Bayesian setting). They are not probabilistic programming languages that provide an object language for inference; […]

## Iterative importance sampling

Aki points us to some papers: Langevin Incremental Mixture Importance Sampling Parallel Adaptive Importance Sampling Iterative importance sampling algorithms for parameter estimation problems Next one is not iterative, but interesting in other way Black-box Importance Sampling Importance sampling is what you call it when you’d like to have draws of theta from some target distribution […]

## Stan Weekly Roundup, 25 August 2017

This week, the entire Columbia portion of the Stan team is out of the office and we didn’t have an in-person/online meeting this Thursday. Mitzi and I are on vacation, and everyone else is either teaching, TA-ing, or attending the Stan course. Luckily for this report, there’s been some great activity out of the meeting […]

## Bigshot statistician keeps publishing papers with errors; is there anything we can do to get him to stop???

OK, here’s a paper with a true theorem but then some false corollaries. First the theorem: The above is actually ok. It’s all true. But then a few pages later comes the false statement: This is just wrong, for two reasons. First, the relevant reference distribution is discrete uniform, not continuous uniform, so the normal […]

## Wolfram on Golomb

I was checking out Stephen Wolfram’s blog and found this excellent obituary of Solomon Golomb, the mathematician who invented the maximum-length linear-feedback shift register sequence, characterized by Wolfram as “probably the single most-used mathematical algorithm idea in history.” But Golomb is probably more famous for inventing polyominoes. The whole thing’s a good read, and it […]

## Stan Weekly Roundup, 28 July 2017

Here’s the roundup for this past week. Michael Betancourt added case studies for methodology in both Python and R, based on the work he did getting the ML meetup together: RStan workflow PyStan workflow Michael Betancourt, along with Mitzi Morris, Sean Talts, and Jonah Gabry taught the women in ML workshop at Viacom in NYC […]

## Animating a spinner using ggplot2 and ImageMagick

It’s Sunday, and I [Bob] am just sitting on the couch peacefully ggplotting to illustrate basic sample spaces using spinners (a trick I’m borrowing from Jim Albert’s book Curve Ball). There’s an underlying continuous outcome (i.e., where the spinner lands) and a quantization into a number of regions to produce a discrete outcome (e.g., “success” […]

## Hey—here are some tools in R and Stan to designing more effective clinical trials! How cool is that?

In statistical work, design and data analysis are often considered separately. Sometimes we do all sorts of modeling and planning in the design stage, only to analyze data using simple comparisons. Other times, we design our studies casually, even thoughtlessly, and then try to salvage what we can using elaborate data analyses. It would be […]