🎲 Probability Theory - gautam6599123

Step-specific activation energies in hydrogen direct reduction of iron ore are identifiable from conversion data via Bayesian inference

📈Statistical Learning Academic

sciencedirect.com·

rishabhbhartiya/STATANIM: A Manim extension for animated statistical visualisations — distributions, regression, probability, inference and more.

🐍Python Code

github.com··Hacker News

Stein’s method, learning and inference -or- how to really monitor convergence and thin chains

📊Optimization Academic

statmodeling.stat.columbia.edu·

The AI Humanization Arms Race: Bypasser Tools vs Detection 2026

🗣️Large Language Models Blog

hub.paper-checker.com·

I Built a Polymarket Trading Bot That Tries to Capture the Last 60 Seconds of Market Inefficiency

⏱️Time Series Analysis Blog

medium.com

··DEV

Xiao-Qing Meng in nLab

🔷Abstract Algebra

ncatlab.org·

The Next Swan: Frank Ramsey, Variable Hypotheticals, and the Bet on Induction

🎯Decision Theory

lesswrong.com·

Bayesian efficient coding as a theory of perception: progress, controversies, and prospects

📡Information Theory

cell.com·

KDM: embedding DNA/RNA motifs and sequences in a shared k-mer space for unified discovery, analysis and binding prediction

🧠Neural Networks Academic

biorxiv.org·

Fast Option Pricing using Fourier Transform

📐Linear Algebra News

vertoxquant.com·

Elements of Cryptanalysis: From Al-Kindi to Post-Quantum Standards — Elements of Cryptanalysis: From Al-Kindi to Post-Quantum Standards

🔢Mathematics

bnaskrecki.faculty.wmi.amu.edu.pl·

Simpson’s Paradox

📈Statistical Learning Academic

plato.stanford.edu··Hacker News

Chavda's Paradox

🔢Mathematics News Blog

zencapital.substack.com··Substack

Where do the major World Cup forecasts disagree? I compared my model against Goldman Sachs, Opta, Polymarket and Others to find out where they all diverge

∂Automatic Differentiation News Blog

oliverssports.substack.com··r/soccer

A multiscale, Bayesian inference approach to augment mechanistic models of cell signaling with machine-learning predictions of binding affinity

📈Statistical Learning

journals.plos.org·

Machine learning from scratch, what to build before using scikit-learn

🤖AI Tutorial

iwtlp.com··DEV

Hidden geometry explains why kernel methods separate complex data so well

📈Statistical Learning

phys.org·

Erratum: Critical Probability Distributions of the Order Parameter from the Functional Renormalization Group [Phys. Rev. Lett. 129 , 210602 (2022)]

🌀Hamiltonian Mechanics

link.aps.org·

Determining universal spectra from probability distributions

Kernel embeddings and the separation of measure phenomenon

Step-specific activation energies in hydrogen direct reduction of iron ore are identifiable from conversion data via Bayesian inference

rishabhbhartiya/STATANIM: A Manim extension for animated statistical visualisations — distributions, regression, probability, inference and more.

Stein’s method, learning and inference -or- how to really monitor convergence and thin chains

The AI Humanization Arms Race: Bypasser Tools vs Detection 2026

I Built a Polymarket Trading Bot That Tries to Capture the Last 60 Seconds of Market Inefficiency

Xiao-Qing Meng in nLab

The Next Swan: Frank Ramsey, Variable Hypotheticals, and the Bet on Induction

Bayesian efficient coding as a theory of perception: progress, controversies, and prospects

KDM: embedding DNA/RNA motifs and sequences in a shared k-mer space for unified discovery, analysis and binding prediction

Fast Option Pricing using Fourier Transform

Elements of Cryptanalysis: From Al-Kindi to Post-Quantum Standards — Elements of Cryptanalysis: From Al-Kindi to Post-Quantum Standards

Simpson’s Paradox

Chavda's Paradox

Where do the major World Cup forecasts disagree? I compared my model against Goldman Sachs, Opta, Polymarket and Others to find out where they all diverge

A multiscale, Bayesian inference approach to augment mechanistic models of cell signaling with machine-learning predictions of binding affinity

Machine learning from scratch, what to build before using scikit-learn

Hidden geometry explains why kernel methods separate complex data so well

Erratum: Critical Probability Distributions of the Order Parameter from the Functional Renormalization Group [Phys. Rev. Lett. 129 , 210602 (2022)]