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statmodeling.stat.columbia.edu /3 weeks ago

Frequentism for Bayesians: He wants to teach frequentist methods to engineering students with a strong Bayesian backgro...

Beyond the teaching question, this is an interesting topic on its own: thinking about classical statistical ideas of point estimation, hypothesis testing, and uncertainty quantific...

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johndcook.com /1 month ago

Posterior mean

Common sense says that what you believe after seeing new data should be some sort of compromise between what you believed before and what the new data says. You don’t want to ignor...

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statmodeling.stat.columbia.edu /1 month ago

“Making Statistics Work: Information Theory and Bayesian Inference”

I took a look at the above-titled book by economists Duncan Foley and Ellis Scharfenaker. It’s an interesting read, in many ways a throwback to the 1950s when a group of mathematic...

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statmodeling.stat.columbia.edu /1 month ago

Reviews of our Bayesian Workflow book from Bin Yu, David Spiegelhalter, Brad Efron, Christian Robert, Rohan Alexander, a...

Roughly speaking, Bayesian Workflow is to Bayesian Data Analysis in 2026 what Bayesian Data Analysis was to earlier Bayesian books in 1995: it builds upon everything that came befo...

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statmodeling.stat.columbia.edu /4 weeks ago

Survey Statistics: wanting workflow

Last week Andrew commented that we need a more transparent workflow for survey statistics. So I looked in the new Bayesian Workflow book: Chapter 19 “Building up to a hierarchical...

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journals.plos.org /6 days ago

A Bayesian framework for multivariate differential analysis

by Marie Chion, Arthur Leroy Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the ma...

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statmodeling.stat.columbia.edu /1 month ago

Posterior predictive checking is for non-Bayesians too!

When I first started working on posterior predictive checking back in 1988, it was as a device for determining equivalent degrees of freedom for a chi-squared test for a model with...

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towardsdatascience.com /1 week ago

Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks

More informed decision-making through uncertainty quantification The post Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks appeared first on Towards D...

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johndcook.com /1 month ago

Posterior variance

A few days ago I wrote a post entitled Does additional data always reduce posterior variance?. In a nutshell, the answer is no, not always. That led the previous post which looked...

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statmodeling.stat.columbia.edu /2 weeks ago

Bayesian Workflow free pdf!

Our wonderful new Bayesian Workflow book is now available as a free pdf! Just go the link—it’s right there! I recommend getting the hard copy too because you’ll want to be able to...

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statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: quantifying uncertainty in ranked choice voting polls

We’ve talked about uncertainty in polls (see Margin of Error, Total Margin of Error, Total Margin of Error II) and we’ve talked about ranked data (see exploded logit !). A new pape...

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digitalthoughtdisruption.com /1 day ago

Bayesian Inference and Predictive Processing: Why AI Needs Evidence

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statmodeling.stat.columbia.edu /2 weeks ago

Survey Statistics: more on SynthMargins and Bayes-Raking

Last week we discussed SynthMargins, a method from the poster Modeling Complex Contingency Tables that uses partial information (margins) about poststratification variables. On the...

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medium.com /2 weeks ago

The Dhahran Probability Density Function: Rethinking Parameter Estimation Beyond Gaussian…

What if the probability distribution underlying your parameter estimation algorithm is the reason you’re getting the wrong answer?Continue reading on Medium »

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statmodeling.stat.columbia.edu /1 month ago

“Placebo tests deserve a model, not just a glance.”

Miha Gazvoda shares this post with the above title and the subtitle, “Using Bayesian multilevel models to correct bias and calibrate uncertainty.” He’s using the chickens model fro...

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towardsdatascience.com /2 weeks ago

Bayesian Guardrails for AI Decisions: Measuring Uncertainty Before Automating Decisions

AI systems should not automate a decision simply because they can provide a prediction. A decision system should consider how uncertain the prediction is and defer if a mistake wou...

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medium.com /1 month ago

Beyond Guesswork — Bayesian Budget Allocation for Paid Search

Using artificial intelligence to decide where the next Euro goes.Continue reading on Berlin Tech Blog (by mobile.de & Kleinanzeigen) »

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statmodeling.stat.columbia.edu /3 weeks ago

Bayesian computation meeting in France in May, 2027

OK, this one’s time constrained so I’ll post it right away, not on the usual lag. Christian Robert points to this conference announcement: We invite proposals for the BayesComp 202...

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statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: structured MRP to smooth survey weights

Last week, Raphael K shared a concern: adjusting for lots of variables can lead to very large weights. So today let’s dive into Si et al. 2020, who saw this in constructing survey...

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marginalrevolution.com /1 month ago

The optimal Bayesian update?

I see at least three updates one might make from the recent OAI/Hugging Face hacking incident: 1. “This happened sooner than I expected, and the story is more dramatic than I expec...

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journals.plos.org /4 weeks ago

Collective posterior inference from highly variable empirical replicates

by Nadav Ben Nun, Saharon Rosset, David Gresham, Yoav Ram High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. S...

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statmodeling.stat.columbia.edu /6 days ago

Confirmation bias in software testing, Walnutpie edition

So, those exciting results I presented at StanCon about Walnutpie? Let’s pull back the curtain. Confirmation bias in testing Those stellar results were the result of a bug in the w...

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medium.com /1 week ago

Day 5/60: Reverse Engineering Reality — Maximum Likelihood Estimation (MLE)

Over the past four days, we established how distributions behave and how to test assumptions about them.Continue reading on Medium »

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medinform.jmir.org /1 week ago

Bayesian Analysis of AI-Driven Cost Savings in UK and Australian Health Care Systems: Cross-Sector Implementation Study

Background: Health care systems face growing fiscal pressure while AI reaches clinical parity in several domains. UK National Health Service expenditure rose by 52%, while Australi...

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