The contraction mapping theorem says that if a function moves points closer together, then there must be some point the function doesn’t move. We’ll make this statement more precise and give a historically important application. Definitions and theorem A function f on a metric space X is a contraction if there exists a constant q with […]

# Category: Statistics

## The causal hype ratchet

Noah Haber informs us of a research article, “Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review,” that he wrote with Emily Smith, Ellen Moscoe, Kathryn Andrews, Robin Audy, Winnie Bell, Alana Brennan, Alexander Breskin, Jeremy Kane, Mahesh Karra, Elizabeth McClure, and Elizabeth Suarez, and […]

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## The causal hype ratchet

Noah Haber informs us of a research article, “Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review,” that he wrote with Emily Smith, Ellen Moscoe, Kathryn Andrews, Robin Audy, Winnie Bell, Alana Brennan, Alexander Breskin, Jeremy Kane, Mahesh Karra, Elizabeth McClure, and Elizabeth Suarez, and […]

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## Max Ent at Max Plank

## “more Bayesian” GANs

On X validated, I got pointed to this recent paper by He, Wang, Lee and Tiang, that proposes a new form of Bayesian GAN. Although I do not see it as really Bayesian, as explained below. “[The] existing Bayesian method (Saatchi & Wilson, 2017) may lead to incompatible conditionals, which suggest that the underlying joint […]

## Grange Thieri [Pic Saint-Loup]

## Exploring model fit by looking at a histogram of a posterior simulation draw of a set of parameters in a hierarchical model

Opher Donchin writes in with a question: We’ve been finding it useful in the lab recently to look at the histogram of samples from the parameter combined across all subjects. We think, but we’re not sure, that this reflects the distribution of that parameter when marginalized across subjects and can be a useful visualization. It […]

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## Exploring model fit by looking at a histogram of a posterior simulation draw of a set of parameters in a hierarchical model

Opher Donchin writes in with a question: We’ve been finding it useful in the lab recently to look at the histogram of samples from the parameter combined across all subjects. We think, but we’re not sure, that this reflects the distribution of that parameter when marginalized across subjects and can be a useful visualization. It […]

The post Exploring model fit by looking at a histogram of a posterior simulation draw of a set of parameters in a hierarchical model appeared first on Statistical Modeling, Causal Inference, and Social Science.

## Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III

Tour III Capability and Severity: Deeper Concepts From the itinerary: A long-standing family feud among frequentists is between hypotheses tests and confidence intervals (CIs), but in fact there’s a clear duality between the two. The dual mission of the first stop (Section 3.7) of this tour is to illuminate both CIs and severity by […]

## Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III

Tour III Capability and Severity: Deeper Concepts From the itinerary: A long-standing family feud among frequentists is between hypotheses tests and confidence intervals (CIs), but in fact there’s a clear duality between the two. The dual mission of the first stop (Section 3.7) of this tour is to illuminate both CIs and severity by […]

## approximate likelihood perspective on ABC

George Karabatsos and Fabrizio Leisen have recently published in Statistics Surveys a fairly complete survey on ABC methods [which earlier arXival I had missed]. Listing within an extensive bibliography of 20 pages some twenty-plus earlier reviews on ABC (with further ones in applied domains)! “(…) any ABC method (algorithm) can be categorized as either (1) […]

## hittin’ a Brexit wall

## Trademark symbol, LaTeX, and Unicode

Earlier this year I was a coauthor on a paper about the Cap Score™ test for male fertility from Androvia Life Sciences [1]. I just noticed today that when I added the publication to my CV, it caused some garbled text to appear in the PDF. Here is the corresponding LaTeX source code. Fixing the […]

## Rotary

We try to keep this blog mostly technical and business (as we assume that is what our readers are here for). However, this post is going to be an exception. I’ve just got back from photographing the Rotary Club of San Francisco‘s 2018 Holiday Party. We had a special guest SF Mayor London Breed (shown … Continue reading Rotary

## RSA with one shared prime

The RSA encryption setup begins by finding two large prime numbers. These numbers are kept secret, but their product is made public. We discuss below just how difficult it is to recover two large primes from knowing their product. Suppose two people share one prime. That is, one person chooses primes p and q and the other chooses p […]

## When “nudge” doesn’t work: Medication Reminders to Outcomes After Myocardial Infarction

Gur Huberman points to this news article by Aaron Carroll, “Don’t Nudge Me: The Limits of Behavioral Economics in Medicine,” which reports on a recent study by Kevin Volpp et al. that set out “to determine whether a system of medication reminders using financial incentives and social support delays subsequent vascular events in patients following […]

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## When “nudge” doesn’t work: Medication Reminders to Outcomes After Myocardial Infarction

Gur Huberman points to this news article by Aaron Carroll, “Don’t Nudge Me: The Limits of Behavioral Economics in Medicine,” which reports on a recent study by Kevin Volpp et al. that set out “to determine whether a system of medication reminders using financial incentives and social support delays subsequent vascular events in patients following […]

The post When “nudge” doesn’t work: Medication Reminders to Outcomes After Myocardial Infarction appeared first on Statistical Modeling, Causal Inference, and Social Science.

## The Netflix Data War

A recent article in the Wall Street Journal, “At Netflix, Who Wins When It’s Hollywood vs. the Algorithm?” by Shalini Ramachandran and Joe Flint details some of the internal debates within Netflix between the Los Angeles-based content tea…

## a glaringly long explanation

It is funny that, when I am teaching the rudiments of Bayesian statistics to my undergraduate students in Paris-Dauphine, including ABC via Rasmus’ socks, specific questions about the book (The Bayesian Choice) start popping up on X validated! Last week was about the proof that ABC is exact when the tolerance is zero. And the […]

## Comparing racism from different eras: If only Tucker Carlson had been around in the 1950s he could’ve been a New York Intellectual.

TV commentator Carlson in 2018 recently raised a stir by saying that immigration makes the United States “poorer, and dirtier, and more divided,” which reminded me of this rant from literary critic Alfred Kazin in 1957: Kazin put it in his diary and Carlson broadcast it on TV, so not quite the same thing. But […]

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