Bayesian Learning and Neural Networks (jupyter book sources)

A quick reference for how to run many models in R.

This repository contains the collection of Cognitive Science computation modeling projects made for the DTU Human-Centered AI course 02458: Cognitive Modeling

Crema: Credal Models Algorithms

Visual Search Model: A Bayesian model for visual search on natural scenes.

Electoral forecasting dashboards

Bayesian MLM approach to sentiment analysis of r/wallstreetbets and Robinhood usage

This repository is a collection of publications related to probabilistic programming languages, probabilistic modelling, inference and criticism of probabilistic models.

Does the mind have statistical models? Even if it does, are these models Bayesian? We explore these questions as a part of my Masters capstone project guided by Prof. Glenn Shafer

Learning hyperparameters in Bayesian models by matching moments of prior predictive distributions.

Tutorials for the synthetic control method for causal inference using PyMC

This repository contains the code for the paper "Improving the Performance of Robust Control through Event-Triggered Learning".

Using Bayesian Modelling to predict Premier League football match wins. See Medium blog post.

RMarkdown source for the "Introduction to Statistics and Data Science Using R" textbook

bayesian | hierarchical-models

This repository contains the deception-game experiment. This game was created as a replication of the first experiment from the original study by Ransom et. al. 2019.

NeuroBio 316QC: Probabilistic models for neural data: from single neurons to population dynamics

This repository includes the code for the paper "Detection of Prostate Cancer with Multi-Parametric MRI Utilizing the Anatomic Structure of the Prostate".

Jupyter notebooks for probabilistic modelling of vibrational spectroscopic datasets

Phenocam forecasting challenge done as part of the NEFI short course in 2020 https://ecoforecast.org/nefi2022/