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towardsdatascience.com /1 month ago

Why You Think Like a Bayesian but Were Taught Like a Frequentist

From a chocolate bar with no price tag to a marketing mix model in PyMC, and the 200-year-old integral that stood in between. The post Why You Think Like a Bayesian but Were Taught...

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statmodeling.stat.columbia.edu /2 weeks ago

Bayesian Workflow code is at the Bayesian Workflow website!

Bayesian Workflow isn’t just a free pdf, it’s also an ecosystem of worked examples with code. Over and over in the book we go through examples really carefully—God is in every leaf...

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journals.plos.org /1 month ago

R-package agentBayes: Likelihood-based statistical methods for agent-based models

by Niklas Moser, Dmitri Finkelshtein, Georgy Chargaziya, Stephen J. Cornell, Sara Hamis, Jacob G. Scott, Dagim Shiferaw Tadele, Otso Ovaskainen Statistically analysing interacting...

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statmodeling.stat.columbia.edu /2 days ago

How to get the most out of Bayesian Data Analysis

Appendix B of our new Bayesian Workflow book is called “How to get the most out of Bayesian Data Analysis”: Our thinking about Bayesian statistics is summarized in the books Bayesi...

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journals.plos.org /1 month 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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towardsdatascience.com /1 month 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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statmodeling.stat.columbia.edu /3 weeks ago

Prior distributions, partial pooling, and reference sets (Bayesians are frequentists)

Sudip Paul writes: I’ve written a blog post arguing that the philosophical reference class problem — which comparison group is “right” for assigning a probability to an individual...

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towardsdatascience.com /1 month ago

The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is

Frequentist confidence intervals and Bayesian credible intervals answer different questions, and confusing them can distort product decisions The post The 95% Illusion: Why Your Co...

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towardsdatascience.com /6 days ago

Inferring the Host: A Bayesian Look at the Monty Hall Problem

"Always switch" is true for one particular host. Watch a few episodes and you can work out which host you've got. The post Inferring the Host: A Bayesian Look at the Monty Hall Pro...

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digitalthoughtdisruption.com /1 month ago

Bayesian Inference and Predictive Processing: Why AI Needs Evidence

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statmodeling.stat.columbia.edu /1 month 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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r-bloggers.com /1 week ago

A Bayesian Network to Explore Data Center Risks

Data centers required to fuel the ambitions of Artificial Intelligence companies have sparked a nationwide conversation. AI companies are moving quickly to build out infrastructure...

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bioengineer.org /1 month ago

Bayesian networks improve probabilistic rainfall inference in Rio de Janeiro

Rio de Janeiro is a city that lives with rain on a knife’s edge. Steep granite massifs plunge into dense coastal neighborhoods, and when moderate storms roll in off the Atlantic, t...

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r-bloggers.com /2 weeks ago

Bayesian workflow [book review]

“This original, thought-provoking, and transforming, book is much much more than an implementation manual for Bayesian Data Analysis, even though it shares almost the same perspect...

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johndcook.com /1 month ago

Bayesian OCR

The Greek letter β (beta) and the German letter ß (eszett) look similar, especially in some fonts. Now suppose an OCR program sees some character that could be a beta or could be a...

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statmodeling.stat.columbia.edu /1 month ago

This is how we do modern frequentist statistics: Using fake-data simulation to understand what can happen in a study

In a famous (to readers of this blog) example of statistical error, a researcher reported that beautiful parents were 36% more likely to have girl babies. The saps at Freakonomics...

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statmodeling.stat.columbia.edu /1 month 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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statmodeling.stat.columbia.edu /2 weeks ago

Survey Statistics: 2 different uses of simulated data

Last month Andrew wrote: I use simulated data to understand how a model works; it can be viewed as a form of mathematical analysis, a way of working out the implications of a set o...

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scienceandculture.com /3 weeks ago

My Mathematical Development: Probability and Improbability

As it is, probabilities are everywhere in information theory, and yet probability theory and information theory have a very different feel. Source

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medium.com /1 month 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 month 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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bioengineer.org /1 week ago

New FLARE Framework Brings Statistical Rigor to Nonlinear Clinical Risk Prediction

A new framework called FLARE combines nonlinear machine learning flexibility with closed-form uncertainty estimates, statistical tests, and patient-level interaction analysis for c...

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r-bloggers.com /3 weeks ago

mlreserve: machine-learning loss reserving on ‘real-world’ triangles (based on a ‘ChainLadder’ fork)

This post presents a function `mlReserve()` for machine-learning loss reserving on 'real-world' triangles, based on a fork of the 'ChainLadder' package. It includes two experiments...

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statmodeling.stat.columbia.edu /1 month ago

Why I don’t like highest posterior density (HPD) intervals

This post is by Bob There are several reasons I prefer central intervals. First, you can’t tell how much of the probability is above or below a highest posterior density interval....

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