Objective is not so objective

Model selection is a difficult process particularly in high dimensional settings, dependent observations, and sparse data regime. In this post, I will discuss a common misconception about selecting models based on values of the objective function generated from optimization algorithms in sparse data settings. TL;DR Don’t do it.

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Optimizing, Sampling, and Choosing Priors

Do you really believe your variance parameter can be anywhere from zero to infinity?

In the past, I’ve often not included priors in my models. I often felt daunted by having to pick sensible priors for my parameters, and I usually fell into the common trap of thinking that no priors or uniform priors are somehow the most objective prior because they “let the data do all the talking.” Recent experiences and have completely changed my thinking on this though.

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Meetup/Webinar on June 28: Understanding the Progression of Alzheimer's

We are excited to announce our Meetup next Thursday at 7 PM EDT titled “Understanding the progression of Alzheimer’s.” The Meetup will be hosted by me and we will be streaming it live for those are unable to attend in person (although it’s always more fun to attend in person and Eric is bringing authentic New York pizza).

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2018 PAGE meeting in Montreux

Last week I attended the PAGE conference in Montreux, Switzerland. PAGE conference organizers love beautiful scenery and parties at least as much as they love the scientific program and this year was no different. Montreux is a gem of a Swiss town overlooking Lake Geneva and the Swiss Alps. It is also the final resting place of one my favorite writers, Vladimir Nabokov, as well as his son Dmitri and his wife Vera.
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Deconstructing Stan Manual Part 2: QR Decomposition

On March 15, we held our second meetup of 2018 covering QR decomposition, simulating correlation matrices using the LKJ distribution, and ending with some general advice about priors.

The slides from the meetup are now available on RPubs. If you have any questions or suggestions, please let us know in the comments.

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PAGANZ 2018

Last month, I was invited to Melbourne for a pharmacometrics conference: Population Approach Group of Australia & New Zealand (PAGANZ) meeting. It was great getting to know some more pharmacometricians and really digging into the problems they face, specifically in statistical inference as applied to PK/PD models.

It was a busy trip with talks / workshops lined up on all three days:

  • One day Stan course as part of Population Analysis Work Shops (PAWS) taught by myself and Sam Brilleman.
    Slides (pdf)

  • ISoP Lecture at PAGANZ titled “Stan Meets Pharmacology.”
    Slides (pdf)

  • Melbourne Stan Meetup talk titled “Understanding lp__: proportionality constants and (automatic) transforms.”
    Slides (pdf)

I wanted to fill in some of the motivation, especially for the pace of the short course.

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Correlation or no correlation, that is the question

A friend asked me about how he should update his beliefs about correlation after seeing some data. In his words:

If I have two variables and I want to express that my prior is that the correlation could be anything between -1 and +1 how would I update this prior based on the observed correlation?

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Deconstructing Stan Manual Part 1: Linear Regression

On February 15, we held our first meetup of 2018 starting a new series called Deconstructing the Stan Manual. During the meetup we coded a Linear Regression model in Stan and fit it to the Wine Quality dataset from the UCI Machine Learning Repository.

The slides from the meetup are now available on RPubs. If you have any questions or suggestions, please let us know in the comments.

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San Francisco, Jan 6-9. Asilomar, Jan 9-12.

We’re headed west for a week!

If you’re in San Francisco or Monterey and want to meet up, please reach out. Both Eric and Daniel are making the trip. We’ll be in town for the J.P. Morgan Healthcare Conference and StanCon 2018.

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Generable and Stan

Stan is freedom-respecting, open-source software. – mc-stan.org

Stan is amazing. It’s our tool of choice for building generative models. The project is open-source and we are committed to supporting the open-source community.

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Why Generable

The more important a decision the more “Bayesian” it is apt to be. —- Irving J. Good

At Generable, formerly Stan Group, we are focused on productizing state-of-the-art generative models for making decisions. We are currently working on a broad class of survival (time to event) and econometric models that encode generative structure that cannot be learned from data alone. These models are special, because they enable us to simulate counterfactual states of the world weighted by their respective probabilities.

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