Cognitive science research workspace — Active Inference, Bayesian modeling, ant behavior, and computational neuroscience

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

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

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

Scripts and processed data for modeling lowland tapir diel activity patterns using Bayesian circular mixed-effects models, including code, visualizations, and reproducible workflows.

Bayesian media mix modeling (MMX) framework with latent state decomposition and SKAN bias corrections for historical & causal attribution

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

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

Adaptive learning classifier using a Beta–Binomial model to estimate student proficiency

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

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

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

Active Inference Pocket Lab — portable simulation and learning environment for Active Inference experiments

Tiny, transparent experiments in Active Inference using pymdp

Probabilistic Graphical Models Project

Daniel Ari Friedman, PhD - Research & Education | 106 publications | Active Inference, entomology, cognitive security | Available for consulting & tutoring

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

RL was cheaper. The heuristic was safer. Neither was correct. POLARIS stress-tests operational policies under chaos, demand spikes, and black swan events — asking one question: which policy survives when everything goes wrong? Built with constrained RL, Bayesian modeling, CVaR risk metrics, and a human-in-the-loop governor.

Bayesian and statistical modeling of regional energy load using public ISO data. Explores seasonal patterns, cross-sectional hierarchies, and probabilistic forecasting across hourly and daily resolution.

Online survey of festival-goers examining how sex, experience, and sexual orientation shape safety perceptions and attitudes toward surveillance and policing at music festivals.