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The real challenge in building reliable AI The post From Possible to Probable AI Models appeared first on Towards Data Science.
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Toward...
Ryan Giordano, Alice Cima, Jared Murray, Erin Hartman, and Avi Feller write: Multilevel regression and poststratification (MrP) has become a workhorse method for estimating populat...
How to make decisions when your spreadsheet is lying about the future The post A Gentle Introduction to Stochastic Programming appeared first on Towards Data Science.
Hierarchical Pattern Learning for Hallucination-Free Text GenerationContinue reading on Medium »
A scenario analysis case study on calibrated uncertainty, historical error, and why some models are most useful when they refuse to forecast. The post When the Uncertainty Is Bigge...
When one is building a model in an attempt to make accurate and as precise as possible predictions of multiple or singular outcomes —…Continue reading on Medium »
John “Bayesian Data Analysis” Carlin writes: Recent developments in the methodology of epidemiological research have emphasized the importance of achieving clarity of purpose by cl...
by Shuze Liu, Trevor Holland, Wei Ji Ma, Luigi Acerbi The perception of the external world relies on integrating information from multiple sensory modalities. To do this effective...
This post is from Bob I’ll let Thomas Wiecki, who is one of the core PyMC devs and one of the partners at PyMC Labs, speak for himself here: Thomas Wiecki. 2026. Alchemize: Transpi...
Choice models are useful for modeling elections (or RuPaul’s Drag Race). Consider vote choice with candidates C = {Left, Right, Other}. “Other” can be a third party, not voting, or...
A couple years ago, Jay Naborn wrote: I am studying people’s preference for categorically correct forecasts (such as getting the winner of a sports game right) over error-minimizin...
Two weeks ago we modeled vote choice with candidates C = {Left, Right, Other} as a multinomial logit: P[voter i chooses candidate c from C] = exp(f(X_ic)) / sum_c’ exp(f(X_ic’)) We...
A practical system design combining open-source Bayesian MMM and GenAI for transparent, vendor independent marketing analytics insights. The post Democratizing Marketing Mix Models...
Part 1: The basics — discretization of time, censoring and the life table The post Discrete Time-To-Event Modeling – Predicting When Something Will Happen appeared first on Towards...
An anonymous statistics student from France sends in the above plots (click twice to see big versions) and writes: I’m trying to push French pollsters to start doing MRP. I made a...
This is Part 2 of our LLM Selection series. If you haven't read Part 1 (The Cost of Wrong Model Selection) and Part 2 (Measuring What Actually Matters), start there. This article a...
When exact answers are too expensive, approximate answers with bounded error save the day — and the server budget. Imagine trying to count every unique visitor to a website that re...
Machine learning models can be confident even when they shouldn't be. This article introduces Deep Evidential Regression (DER), a method that lets neural networks rapidly express w...
by Maria-Veronica Ciocanel, John T. Nardini, Kevin B. Flores, Erica M. Rutter, Suzanne S. Sindi, Alexandria Volkening Agent-based modeling (ABM) is a powerful tool for understandi...
Sample size calculation is one of those things that feels solved. You open a textbook, pick a formula, plug in your numbers, and move on…Continue reading on Medium »
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
Why Financial Risk Models Must Go Causal — And What the Industry Gains When They Do The financial i
What if an unsupervised model could become a strong classifier with only a handful of labels? The post You Don’t Need Many Labels to Learn appeared first on Towards Data Science.
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