bayesian-modeling

Cognitive ecosystem framework pairing Active Inference agent models with an Obsidian-compatible markdown knowledge graph — generates executable Python from markdown specs, runs multi-agent simulations, and auto-validates probabilistic constraints.

Hierarchical Bayesian modeling toolkit for interoceptive psychophysics (HRDT & RRST). Includes Stan models, power analysis tools, and educational resources for researchers.

Football forecasting framework to simulate the FIFA World Cup using team strength modeling and probabilistic match prediction.

Agent-based political election simulator using predictive coding

An R package for modeling asymmetric spatial associations between cell types in tissue images using a multilevel Bayesian framework.

GitHub profile and research portfolio for Daniel Ari Friedman (Active Inference Institute) — indexes 100+ software projects, publications with DOIs, and 500+ livestream transcripts across active inference, entomology, cognitive security, and computational biology in Python, Rust, and Lean.

Inflation forecasting during crisis periods using Bayesian Dynamic Linear Models, traditional econometrics, and machine learning. Includes data, code, and comprehensive analysis report.

The project focuses on the analysis of experimental data regarding the continuous biomass production of a cyanobacterium of the genus Nostoc. The primary objective is to understand how different cultivation factors influence biological growth over time, monitored through optical density (OD).

Three‑level hierarchical enactive inference model of mental action (focused‑attention meditation with expert–novice profiles) with reproducible simulations and figures.

computational model showing how compulsive urges might arise when the brain stops listening to its own body signals, testing this idea against real physiological data like heart rate and pupil size.

Bayesian uncertainty, calibration, and hallucination-risk modeling for reliable language and multimodal AI.

Comprehensive analysis of differential gene expression using Bayesian statistics and advanced statistical modeling techniques. The project includes scripts, data, figures, and analysis results.

R code and Stan models for "Decoupling Decision and Intensity in Visual Attention" .

Quantitative football value betting framework in PyTorch & C++20. Features empirical Bayesian Shrinkage (logistic regression), 100% blind out-of-sample backtesting (+17.08% ROI at EV > 5.5%), and a 10,000-run dual-module Monte Carlo simulation engine.

Stan implementation of Lee & Sarnecka's (2010, 2011) knower-level model

Spin — a browser-based pocket lab for learning active inference and the free energy principle, with 10 chapter modules each rendered in three switchable streams (conversational, animated, LaTeX academic). Installable PWA built on React 19, Vite 8, Framer Motion, and KaTeX; 65 Vitest tests.

A repository for the R code and data used in Iwasaki et al. (2023)

Bayesian Marketing Mix Modeling project using PyMC to estimate marketing channel effectiveness through probabilistic inference, adstock transformation, uncertainty-aware ROI analysis, and seasonal trend modelling across multiple advertising channels.

Research on cross-validation reliability in Kaggle tabular classification using 3,000 model runs and empirical Bayesian melding.

Reproducible NCAA basketball prediction research with leakage-safe ETL/features, statistical + ML benchmarking, robustness analysis, locked holdouts, and a 2026 thesis.