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Archive of posts filed under the Bayesian Statistics category.

Comparing the full model to the partial model

Pat Lawlor writes: We are writing with a question about model comparison and fitting. We work in a group at Northwestern that does neural data analysis and modeling, and often would like to compare full models (e.g. neurons care about movement and vision) with various partial models (e.g. they only care about movement). We often […]

Stan is Turing Complete. So what?

This post is by Bob Carpenter. Stan is Turing complete! There seems to a persistent misconception that Stan isn’t Turing complete.1, 2 My guess is that it stems from Stan’s (not coincidental) superficial similarity to BUGS and JAGS, which provide directed graphical model specification languages. Stan’s Turing completeness follows from its support of array data […]

Bayes in the research conversation

Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) […]

Regression and causality and variable ordering

Bill Harris wrote in with a question: David Hogg points out in one of his general articles on data modeling that regression assumptions require one to put the variable with the highest variance in the ‘y’ position and the variable you know best (lowest variance) in the ‘x’ position. As he points out, others speak […]

Identifying pathways for managing multiple disturbances to limit plant invasions

Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write: We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We […]

All the Assumptions That Are My Life

Statisticians take tours in other people’s data. All methods of statistical inference rest on statistical models. Experiments typically have problems with compliance, measurement error, generalizability to the real world, and representativeness of the sample. Surveys typically have problems of undercoverage, nonresponse, and measurement error. Real surveys are done to learn about the general population. But […]

Why we hate stepwise regression

Haynes Goddard writes: I have been slowly working my way through the grad program in stats here, and the latest course was a biostats course on categorical and survival analysis. I noticed in the semi-parametric and parametric material (Wang and Lee is the text) that they use stepwise regression a lot. I learned in econometrics […]

Bayesian nonparametric weighted sampling inference

Yajuan Si, Natesh Pillai, and I write: It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference using inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the […]

WAIC and cross-validation in Stan!

Aki and I write: The Watanabe-Akaike information criterion (WAIC) and cross-validation are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model. WAIC is based on the series expansion of leave-one-out cross-validation (LOO), and asymptotically they are equal. With finite data, WAIC and cross-validation address different predictive questions and thus it is useful […]

Models with constraints

I had an interesting conversation with Aki about monotonicity constraints. We were discussing a particular set of Gaussian processes that we were fitting to the arsenic well-switching data (the example from the logistic regression chapter in my book with Jennifer) but some more general issues arose that I thought might interest you. The idea was […]