Hierarchical Bayesian modeling toolkit for interoceptive psychophysics (HRDT & RRST). Includes Stan models, power analysis tools, and educational resources for researchers.
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Updated
Mar 6, 2026 - HTML
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
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)
ai-tutoringDaniel Ari Friedman, PhD - AI Researcher & Consultant | 106 publications | Active Inference, entomology, cognitive security | Stanford PhD | Available for consulting & tutoring
Scripts and processed data for modeling lowland tapir diel activity patterns using Bayesian circular mixed-effects models, including code, visualizations, and reproducible workflows.
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.
Probabilistic Graphical Models Project
An R package for modeling asymmetric spatial associations between cell types in tissue images using a multilevel Bayesian framework.
Football forecasting framework to simulate the FIFA World Cup using team strength modeling and probabilistic match prediction.
Online survey of festival-goers examining how sex, experience, and sexual orientation shape safety perceptions and attitudes toward surveillance and policing at music festivals.
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.
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