Bayesian Learning and Neural Networks (jupyter book sources)

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

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

Crema: Credal Models Algorithms

Electoral forecasting dashboards

Tutorials for the synthetic control method for causal inference using PyMC

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

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

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

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

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

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

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

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

bayesian | hierarchical-models

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

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

Jupyter notebooks for probabilistic modelling of vibrational spectroscopic datasets

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

A 3D multiplayer driving game - Built with Mapbox, Three.js, and .NET SignalR -Just pure vibes and endless roads to discover

An R package for Bayesian semi-parametric modelling of in-vitro drug combination experiments

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Supplementary material for Donatelli, A., Ćirović, D., Haroldson, M. A., Huber, D., Kindberg, J., Kojola, I., Kusak, J., Mastrantonio, G., Ordiz, A., Reljić, S., Santini, L., van Manen, F. T. & Ciucci, P. The diel niche of brown bears: Constraints on adaptive capacity in human-modified landscapes. Ecography

Some models for information retrieval in Spanish text corpus of the Cuban social network

Bayesian Data Analysis, Model Comparison, Markov Chain Monte Carlo, JAGS

Bayesian models; analytical statistics; stochastic growth; zero-sightings; pest eradication success; population absence; biosecurity; probability generating functions;

Portfolio assignments using Bayesian modeling within cognitive science field. 2020 spring semester at Aarhus University

Supplementary material for Donatelli, A., Mastrantonio, G. & Ciucci, P. Circadian activity of small brown bear populations living in human-dominated landscapes. Sci Rep 12, 15804 (2022). https://doi.org/10.1038/s41598-022-20163-1