This article is a Python copying activity record of Chapter 9, Part 3: 'Logistic Regression Model' from the book 'Introduction to Data Analysis with Bayesian Statistical Modeling using R and Stan'.

I work in materials development at a chemical manufacturer and spend my days thinking about how to apply AI to research. In this note, I have been writing a series on the theme of "Self-Driving Labs ...

Aether AI, founded by UCSD professor Biwei Huang, closed a $20 million seed round on June 18, 2026 to build causal world models that understand cause-and-effect relationships rather than statistical ...

Abstract: This article proposes a robust topology change-aware distribution system state estimation (DSSE) method based on a physics-informed graph neural network and Bayesian Probability Weighted ...

Software is a set of computer instructions and can refer to executable programs, scripts and libraries. By using deep learning sequence models, we predict non-coding variant effects across the allele ...

End-to-end A/B test analysis for a (fictional) streaming subscription product. Demonstrates the full product data scientist workflow: experiment design, data quality, frequentist & Bayesian analysis, ...

The project trains a Bayesian CNN (a CNN with dropout used as approximate Bayesian inference via MC-dropout) and uses its predictive uncertainty to decide which unlabelled images are most worth ...

cDivision of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo 108-0071, Japan dDepartment of Special Pathogens, International Research ...

Bayesian frameworks directly address these challenges by providing (1) uncertainty quantification and (2) sample-efficient exploration of sequence space. BayeStab couples graph features with ...