Category: Bayesian Statistics

Why are functional programming languages so popular in the programming languages community?

Matthijs Vákár writes: Re the popularity of functional programming and Church-style languages in the programming languages community: there is a strong sentiment in that community that functional programming provides important high-level primitives that make it easy to write correct programs. This is because functional code tends to be very short and easy to reason about […]

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Bayesian inference and religious belief

We’re speaking here not of Bayesianism as a religion but of the use of Bayesian inference to assess or validate the evidence regarding religious belief, in short, the probability that God !=0 or the probability that the Pope is Catholic or, as Tyler Cowen put it, the probability that Lutheranism is true. As a statistician […]

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Cool postdoc position in Arizona on forestry forecasting using tree ring models!

Margaret Evans sends in this cool job ad: Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity […]

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N=1 survey tells me Cynthia Nixon will lose by a lot (no joke)

Yes, you can learn a lot from N=1, as long as you have some auxiliary information. The other day I was talking with a friend who’s planning to vote for Andrew Cuomo in the primary. What about Cynthia Nixon? My friend wasn’t even considering voting for her. Now, my friend is, I think, in the […]

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Discussion of effects of growth mindset: Let’s not demand unrealistic effect sizes.

Shreeharsh Kelkar writes: As a regular reader of your blog, I wanted to ask you if you had taken a look at the recent debate about growth mindset [see earlier discussions here and here] that happened on theconversation.com. Here’s the first salvo by Brooke McNamara, and then the response by Carol Dweck herself. The debate […]

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Against Arianism 2: Arianism Grande

“There’s the part you’ve braced yourself against, and then there’s the other part” – The Mountain Goats My favourite genre of movie is Nicole Kidman in a questionable wig. (Part of the sub-genre founded by Sarah Paulson, who is the patron saint of obvious wigs.) And last night I was in the same room* as […]

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“Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models”

Aki points us to this paper by Tore Selland Kleppe, which begins: Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterisation so that the re-parameterised target distribution has close to constant […]

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StanCon 2018 Helsinki tutorial videos online

StanCon 2018 Helsinki tutorial videos are now online at Stan YouTube channel List of tutorials at StanCon 2018 Helsinki Basics of Bayesian inference and Stan, parts 1 + 2, Jonah Gabry & Lauren Kennedy Hierarchical models, parts 1 + 2, Ben Goodrich Stan C++ development: Adding a new function to Stan, parts 1 + 2, […]

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Hey—take this psychological science replication quiz!

Rob Wilbin writes: I made this quiz where people try to guess ahead of time which results will replicate and which won’t in order to give then a more nuanced understanding of replication issues in psych. Based on this week’s Nature replication paper. It includes quotes and p-values from the original study if people want […]

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“To get started, I suggest coming up with a simple but reasonable model for missingness, then simulate fake complete data followed by a fake missingness pattern, and check that you can recover your missing-data model and your complete data model in that fake-data situation. You can then proceed from there. But if you can’t even do it with fake data, you’re sunk.”

Alex Konkel writes on a topic that never goes out of style: I’m working on a data analysis plan and am hoping you might help clarify something you wrote regarding missing data. I’m somewhat familiar with multiple imputation and some of the available methods, and I’m also becoming more familiar with Bayesian modeling like in […]

The post “To get started, I suggest coming up with a simple but reasonable model for missingness, then simulate fake complete data followed by a fake missingness pattern, and check that you can recover your missing-data model and your complete data model in that fake-data situation. You can then proceed from there. But if you can’t even do it with fake data, you’re sunk.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

Bayesian model comparison in ecology

Conor Goold writes: I was reading this overview of mixed-effect modeling in ecology, and thought you or your blog readers may be interested in their last conclusion (page 35): Other modelling approaches such as Bayesian inference are available, and allow much greater flexibility in choice of model structure, error structure and link function. However, the […]

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The fallacy of the excluded middle — statistical philosophy edition

I happened to come across this post from 2012 and noticed a point I’d like to share again. I was discussing an article by David Cox and Deborah Mayo, in which Cox wrote: [Bayesians’] conceptual theories are trying to do two entirely different things. One is trying to extract information from the data, while the […]

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The fallacy of the excluded middle — statistical philosophy edition

I happened to come across this post from 2012 and noticed a point I’d like to share again. I was discussing an article by David Cox and Deborah Mayo, in which Cox wrote: [Bayesians’] conceptual theories are trying to do two entirely different things. One is trying to extract information from the data, while the […]

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Three informal case studies: (1) Monte Carlo EM, (2) a new approach to C++ matrix autodiff with closures, (3) C++ serialization via parameter packs

Andrew suggested I cross-post these from the Stan forums to his blog, so here goes. Maximum marginal likelihood and posterior approximations with Monte Carlo expectation maximization: I unpack the goal of max marginal likelihood and approximate Bayes with MMAP and Laplace approximations. I then go through the basic EM algorithm (with a traditional analytic example […]

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“The most important aspect of a statistical analysis is not what you do with the data, it’s what data you use” (survey adjustment edition)

Dean Eckles pointed me to this recent report by Andrew Mercer, Arnold Lau, and Courtney Kennedy of the Pew Research Center, titled, “For Weighting Online Opt-In Samples, What Matters Most? The right variables make a big difference for accuracy. Complex statistical methods, not so much.” I like most of what they write, but I think […]

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