Using Bayesian Modelling to predict Premier League football match wins. See Medium blog post.
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Updated
Apr 2, 2024 - Jupyter Notebook
Using Bayesian Modelling to predict Premier League football match wins. See Medium blog post.
Tutorials for the synthetic control method for causal inference using PyMC
Crema: Credal Models Algorithms
Bayesian Learning and Neural Networks (jupyter book sources)
Visual Search Model: A Bayesian model for visual search on natural scenes.
This repository contains the code for the paper "Improving the Performance of Robust Control through Event-Triggered Learning".
Phenocam forecasting challenge done as part of the NEFI short course in 2020 https://ecoforecast.org/nefi2022/
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
Some models for information retrieval in Spanish text corpus of the Cuban social network
Electoral forecasting dashboards
NeuroBio 316QC: Probabilistic models for neural data: from single neurons to population dynamics
RMarkdown source for the "Introduction to Statistics and Data Science Using R" textbook
Portfolio assignments using Bayesian modeling within cognitive science field. 2020 spring semester at Aarhus University
Bayesian models; analytical statistics; stochastic growth; zero-sightings; pest eradication success; population absence; biosecurity; probability generating functions;
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.
This repository is a collection of publications related to probabilistic programming languages, probabilistic modelling, inference and criticism of probabilistic models.
An R package for Bayesian semi-parametric modelling of in-vitro drug combination experiments
Bayesian MLM approach to sentiment analysis of r/wallstreetbets and Robinhood usage
Jupyter notebooks for probabilistic modelling of vibrational spectroscopic datasets
Learning hyperparameters in Bayesian models by matching moments of prior predictive distributions.
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